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Will Hulu Jump to VVC (H.266) Before 8K? A 2025 Codec Roadmap and Interim AI Savings

Introduction

The streaming industry stands at a critical crossroads in 2025. While VVC (H.266) promises up to 50% bitrate reduction over HEVC, hardware support remains virtually nonexistent and royalty structures create deployment barriers. (The State of VVC Adoption and Implementation) Meanwhile, AV1 hardware decode on mobile devices is stuck in the mid-to-low teens as of 2025, and VVC is at zero. (Software Decoding and the Future of Mobile Video)

For streaming giants like Hulu, which faces the discontinuation of its standalone app in 2026 as Disney consolidates its streaming strategy, codec decisions carry enormous weight. (Standalone Hulu app will disappear from devices in 2026) The question isn't just about technical superiority—it's about balancing compression efficiency, device compatibility, and cost optimization while maintaining quality standards that keep subscribers engaged.

This analysis examines Hulu's potential codec migration path, comparing VVC, AV1, and HEVC using 2025 peer-reviewed BD-rate data, while exploring how AI-powered preprocessing solutions can deliver immediate bandwidth savings without disrupting existing workflows. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The Current Codec Landscape: Performance vs. Practicality

VVC's Technical Promise and Real-World Hurdles

VVC's architecture and new coding tools have contributed to a quality improvement of 40% over HEVC, with only a 1.6x decode complexity versus HEVC. (The State of VVC Adoption and Implementation) This represents a significant leap in compression efficiency, potentially revolutionizing how streaming services manage bandwidth costs and quality delivery.

However, the reality of deployment tells a different story. Some VVC IP owners like Kwai, ByteDance, and Tencent are deploying VVC with software decode, but these implementations remain limited to specific use cases and controlled environments. (Software Decoding and the Future of Mobile Video)

The royalty landscape adds another layer of complexity. Access Advance LLC announced that both HEVC Advance and VVC Advance Patent Pools opted to maintain their current royalty rates and caps for Licensees who sign before December 31, 2025. (Access Advance Announces HEVC Advance and VVC Advance Pricing through 2030) While this provides some pricing certainty, the cumulative cost of licensing multiple patent pools remains a significant consideration for large-scale deployments.

AV1's Growing Momentum Despite Hardware Limitations

Meta has aggressively started distributing AV1 streams for software-only playback and has co-created an open source project called VCAT (Video Codec Acid Test) to benchmark mobile devices. (Software Decoding and the Future of Mobile Video) This aggressive push demonstrates how major platforms are willing to accept the computational overhead of software decoding to achieve better compression ratios.

The strategic advantage of AV1 lies not just in its royalty-free status, but in its growing ecosystem support. While hardware decode support remains limited, the software implementations have matured significantly, making it a viable option for services willing to invest in client-side processing power.

HEVC's Continued Relevance

Despite being overshadowed by newer codecs, HEVC maintains its position as the workhorse of modern streaming. Its widespread hardware support across devices makes it the safe choice for services prioritizing compatibility over cutting-edge compression efficiency. For Hulu's diverse device ecosystem, HEVC provides the reliability needed to serve content across everything from smart TVs to mobile devices without compatibility concerns.

Hulu's Current Codec Strategy and Device Ecosystem

Understanding Hulu's Technical Infrastructure

Hulu provides users with the right to opt out of targeted advertising, selling, or sharing of personal information, indicating a sophisticated data management infrastructure that extends to content delivery optimization. (Help Center Article Detail) This infrastructure complexity suggests that codec decisions must account for not just technical performance, but also data privacy and user experience considerations.

The upcoming consolidation of Hulu into Disney's broader streaming ecosystem adds urgency to codec standardization decisions. Disney is planning to discontinue the standalone Hulu app in 2026, although the Hulu brand will continue to exist. (Standalone Hulu app will disappear from devices in 2026) This transition period presents both challenges and opportunities for codec optimization.

Device Compatibility Considerations

Streaming services must balance compression efficiency with device compatibility. The reality is that premium compression comes with computational costs that not all devices can handle gracefully. This is where AI-powered preprocessing solutions can provide immediate value by optimizing content for existing codecs rather than waiting for next-generation codec adoption. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

CDN Cost Optimization: The Hidden Driver of Codec Decisions

The Economics of Bandwidth

Content Delivery Network (CDN) costs can be reduced without compromising website speed and reliability through strategic codec selection and optimization. (Optimize CDN Costs: Proven Techniques for Big Savings) For streaming services handling petabytes of data monthly, even small percentage improvements in compression efficiency translate to substantial cost savings.

CDN providers use unique pricing models such as Pay-As-You-Go, Committed Contracts, Tiered Pricing, and Location-Based Pricing. (Optimize CDN Costs: Proven Techniques for Big Savings) Understanding these models is crucial for streaming services evaluating the ROI of codec upgrades versus alternative optimization strategies.

Telco-CDN Efficiency Challenges

Video services are increasingly shifting towards IP-based platforms, leading to a surge in IP traffic and a significant challenge for network operators. (Offloading in Telco-CDNs: Maximizing Efficiency) This shift puts additional pressure on streaming services to optimize their content delivery strategies, making codec efficiency a critical component of infrastructure planning.

Network operators invest in Telco-CDNs to handle growing traffic, but finding the right level of infrastructure can be difficult. Overcommitting can be costly, while under-committing leads to insufficient capacity. (Offloading in Telco-CDNs: Maximizing Efficiency) This infrastructure challenge creates opportunities for streaming services that can reduce their bandwidth requirements through better compression or preprocessing optimization.

AI-Powered Interim Solutions: Bridging the Codec Gap

The Role of Preprocessing in Bandwidth Optimization

While the industry debates the merits of VVC versus AV1, AI-powered preprocessing solutions offer immediate bandwidth reduction benefits that work with existing codec infrastructure. These solutions can reduce video bandwidth requirements by 22% or more while boosting perceptual quality, providing a codec-agnostic approach to optimization. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The advantage of preprocessing solutions lies in their ability to slip in front of any encoder—H.264, HEVC, AV1, AV2 or custom—so streamers can eliminate buffering and shrink CDN costs without changing their existing workflows. (Understanding Bandwidth Reduction for Streaming with AI Video Codec) This approach allows services like Hulu to achieve immediate cost savings while evaluating longer-term codec migration strategies.

Addressing AI-Generated Content Challenges

The rise of AI-generated video content presents unique compression challenges. AI video content often contains artifacts and quality issues that traditional codecs struggle to handle efficiently. (Midjourney AI Video on Social Media: Fixing AI Video Quality) As more platforms incorporate AI-generated content, preprocessing solutions that can clean up these artifacts before encoding become increasingly valuable.

Benchmarked solutions using Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set provide verified performance metrics via VMAF/SSIM metrics and golden-eye subjective studies. (Understanding Bandwidth Reduction for Streaming with AI Video Codec) This comprehensive testing approach ensures that optimization solutions work across diverse content types and quality standards.

A Phased Migration Strategy for Hulu

Phase 1: Immediate AI Preprocessing Implementation

The most practical first step for Hulu involves implementing AI-powered preprocessing solutions that can work with their existing H.264 and HEVC infrastructure. This approach provides immediate bandwidth savings without requiring device compatibility testing or codec licensing negotiations. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Key implementation steps include:

Device telemetry tagging: Implement comprehensive device capability detection to optimize preprocessing parameters for different device classes
Test-asset selection: Choose representative content samples across genres, resolutions, and complexity levels for preprocessing validation
KPI target establishment: Set measurable goals for bandwidth reduction, quality maintenance, and user experience metrics

Phase 2: Selective AV1 Deployment

Once preprocessing optimization is established, Hulu can begin selective AV1 deployment for devices and scenarios where software decoding is acceptable. This phase should focus on:

High-end mobile devices with sufficient processing power
Connected TV platforms with dedicated decoding capabilities
Premium content where quality improvements justify computational overhead

The software-only playback approach pioneered by Meta demonstrates the viability of this strategy, particularly for services willing to invest in client-side optimization. (Software Decoding and the Future of Mobile Video)

Phase 3: VVC Evaluation for 8K Content

VVC's superior compression efficiency makes it particularly attractive for 8K content delivery, where bandwidth requirements would otherwise be prohibitive. However, this deployment should be limited to:

Premium 8K content where quality justifies the complexity
Devices with confirmed VVC hardware support (as it becomes available)
Selective tile-based encoding for specific content regions

VVC's improved quality and efficiency have led to several early deployments and efficient software-only playback on mobile devices, but these remain experimental rather than production-scale implementations. (The State of VVC Adoption and Implementation)

Technical Implementation Considerations

Device Capability Detection and Adaptive Streaming

Successful codec migration requires sophisticated device capability detection that goes beyond simple codec support flags. Modern adaptive streaming systems must consider:

Processing power available for software decoding
Battery life implications of computational overhead
Network conditions and bandwidth availability
User preferences and quality settings

This complexity is where AI-powered solutions can provide additional value by optimizing content preprocessing based on detected device capabilities and network conditions. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Quality Assurance and Monitoring

Implementing new codec strategies requires comprehensive quality monitoring systems that can detect degradation across diverse content types and viewing conditions. The challenge extends beyond traditional metrics to include:

Perceptual quality assessment across different content genres
Playback performance monitoring across device types
User engagement correlation with quality changes
Cost-benefit analysis of compression versus quality trade-offs

Solutions that have been verified via VMAF/SSIM metrics and golden-eye subjective studies provide the reliability needed for production deployments. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Industry Partnerships and Ecosystem Development

The Importance of Collaborative Development

The streaming industry's codec evolution benefits from collaborative development approaches that avoid fragmentation. Meta's co-creation of the VCAT (Video Codec Acid Test) project demonstrates how industry leaders can work together to establish benchmarking standards that benefit the entire ecosystem. (Software Decoding and the Future of Mobile Video)

This collaborative approach extends to AI-powered optimization solutions, where partnerships with cloud providers and technology accelerators can provide the infrastructure and expertise needed for large-scale deployment. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Leveraging Cloud Infrastructure for Optimization

Cloud-based preprocessing and optimization solutions offer streaming services the flexibility to implement advanced compression techniques without significant infrastructure investment. These solutions can be integrated into existing workflows through SDK/API implementations that maintain compatibility with current encoding pipelines. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Measuring Success: KPIs and Performance Metrics

Establishing Baseline Measurements

Before implementing any codec migration strategy, streaming services must establish comprehensive baseline measurements that capture:

Current bandwidth utilization across content types and device categories
Quality metrics using standardized assessment tools
User engagement and satisfaction scores
CDN costs and infrastructure utilization
Device compatibility and playback success rates

Defining Success Criteria for Each Migration Phase

Each phase of codec migration should have specific, measurable success criteria:

Phase 1 (AI Preprocessing):

20%+ reduction in bandwidth requirements without quality degradation
Maintained or improved user engagement metrics
Reduced CDN costs with ROI achievement within 6 months
Zero increase in playback failures or compatibility issues

Phase 2 (AV1 Deployment):

Successful software decoding on target device categories
Additional 15-25% bandwidth reduction over preprocessing alone
Maintained battery life performance on mobile devices
Positive user experience metrics for quality-sensitive content

Phase 3 (VVC Evaluation):

Demonstrated 8K content delivery feasibility
Cost-effective compression for premium content tiers
Hardware support validation as devices become available
Scalable implementation pathway for broader deployment

Future-Proofing Streaming Infrastructure

Preparing for Next-Generation Content

As streaming services prepare for 8K content, HDR expansion, and immersive video formats, codec strategy becomes increasingly critical. The bandwidth requirements for these advanced formats make compression efficiency not just a cost optimization but a technical necessity for widespread deployment.

AI-powered preprocessing solutions provide a bridge technology that can optimize content for any target codec, ensuring that infrastructure investments remain valuable as codec standards evolve. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Maintaining Competitive Advantage

In an increasingly competitive streaming market, technical differentiation through superior compression and quality delivery can provide significant advantages. Services that can deliver higher quality content at lower bandwidth costs can offer better user experiences while maintaining healthier profit margins.

The key is implementing solutions that provide immediate benefits while maintaining flexibility for future codec adoption. This approach allows services to optimize their current infrastructure while preparing for next-generation technologies. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Conclusion: A Pragmatic Path Forward

Hulu's codec roadmap should prioritize practical solutions that deliver immediate value while preparing for future technologies. The evidence suggests that VVC adoption will be gradual, limited initially to specific use cases like 8K content where its superior compression efficiency justifies the implementation complexity.

The most pragmatic approach involves a three-phase strategy: immediate implementation of AI-powered preprocessing for existing codecs, selective AV1 deployment where software decoding is viable, and careful VVC evaluation for premium content applications. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

This strategy acknowledges the reality that codec migration is not just a technical decision but a business optimization challenge that must balance compression efficiency, device compatibility, licensing costs, and user experience. By implementing codec-agnostic optimization solutions first, streaming services can achieve immediate bandwidth savings while maintaining the flexibility to adopt new codecs as the ecosystem matures. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The streaming industry's future lies not in choosing a single "winning" codec, but in developing intelligent, adaptive systems that can optimize content delivery across multiple codecs and device capabilities. Services that embrace this flexibility while implementing immediate optimization solutions will be best positioned to navigate the evolving codec landscape successfully.

Frequently Asked Questions

What is VVC (H.266) and how does it compare to HEVC for streaming?

VVC (Versatile Video Coding) is a next-generation codec that offers up to 50% bitrate reduction over HEVC with 40% quality improvement. However, VVC currently has zero hardware decode support on mobile devices as of 2025, while HEVC enjoys widespread adoption. Despite this limitation, VVC's architecture provides only 1.6x decode complexity versus HEVC, making software-only playback feasible on modern devices.

Why might Hulu delay VVC adoption despite its efficiency benefits?

Hulu faces several barriers to VVC adoption including virtually nonexistent hardware support, complex royalty structures, and the upcoming discontinuation of the standalone Hulu app in 2026. With Disney planning to integrate Hulu into their broader ecosystem, the timing for major codec transitions becomes more strategic. Additionally, AV1 hardware decode on mobile devices remains stuck in the mid-to-low teens, suggesting similar challenges for VVC deployment.

How can AI preprocessing help reduce streaming bandwidth costs before VVC adoption?

AI preprocessing techniques can provide immediate bandwidth savings while streaming platforms wait for next-generation codec adoption. These solutions optimize video content before encoding, reducing file sizes without compromising quality. AI-powered bandwidth reduction for streaming represents a practical interim solution that can deliver cost savings and improved performance without requiring new hardware support or complex licensing negotiations.

What role does software decoding play in the future of mobile video streaming?

Software decoding is becoming increasingly important as hardware support lags behind codec innovation. Meta has aggressively started distributing AV1 streams for software-only playback and co-created VCAT (Video Codec Acid Test) to benchmark mobile devices. Some VVC IP owners like Kwai, ByteDance, and Tencent are already deploying VVC with software decode, demonstrating that efficient software-only playback is viable for modern codecs.

How do CDN costs factor into codec migration decisions for streaming services?

CDN costs significantly impact codec migration strategies as different pricing models affect bandwidth economics. CDN providers use Pay-As-You-Go, Committed Contracts, Tiered Pricing, and Location-Based Pricing models. More efficient codecs like VVC can reduce bandwidth usage by up to 50%, potentially leading to substantial CDN cost savings. However, the deployment complexity and hardware support requirements must be weighed against these potential savings.

What is the current state of VVC patent licensing and royalty structures?

Access Advance LLC announced that both HEVC Advance and VVC Advance Patent Pools will maintain current royalty rates and caps for licensees who sign before December 31, 2025. An incentive aligns royalty caps for the Multi-Codec Bridging Agreement (MCBA) and VVC Advance Patent Pool for qualifying licensees. These licensing structures create deployment barriers that streaming services must navigate when planning codec migrations.

Sources

Will Hulu Jump to VVC (H.266) Before 8K? A 2025 Codec Roadmap and Interim AI Savings

Introduction

The streaming industry stands at a critical crossroads in 2025. While VVC (H.266) promises up to 50% bitrate reduction over HEVC, hardware support remains virtually nonexistent and royalty structures create deployment barriers. (The State of VVC Adoption and Implementation) Meanwhile, AV1 hardware decode on mobile devices is stuck in the mid-to-low teens as of 2025, and VVC is at zero. (Software Decoding and the Future of Mobile Video)

For streaming giants like Hulu, which faces the discontinuation of its standalone app in 2026 as Disney consolidates its streaming strategy, codec decisions carry enormous weight. (Standalone Hulu app will disappear from devices in 2026) The question isn't just about technical superiority—it's about balancing compression efficiency, device compatibility, and cost optimization while maintaining quality standards that keep subscribers engaged.

This analysis examines Hulu's potential codec migration path, comparing VVC, AV1, and HEVC using 2025 peer-reviewed BD-rate data, while exploring how AI-powered preprocessing solutions can deliver immediate bandwidth savings without disrupting existing workflows. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The Current Codec Landscape: Performance vs. Practicality

VVC's Technical Promise and Real-World Hurdles

VVC's architecture and new coding tools have contributed to a quality improvement of 40% over HEVC, with only a 1.6x decode complexity versus HEVC. (The State of VVC Adoption and Implementation) This represents a significant leap in compression efficiency, potentially revolutionizing how streaming services manage bandwidth costs and quality delivery.

However, the reality of deployment tells a different story. Some VVC IP owners like Kwai, ByteDance, and Tencent are deploying VVC with software decode, but these implementations remain limited to specific use cases and controlled environments. (Software Decoding and the Future of Mobile Video)

The royalty landscape adds another layer of complexity. Access Advance LLC announced that both HEVC Advance and VVC Advance Patent Pools opted to maintain their current royalty rates and caps for Licensees who sign before December 31, 2025. (Access Advance Announces HEVC Advance and VVC Advance Pricing through 2030) While this provides some pricing certainty, the cumulative cost of licensing multiple patent pools remains a significant consideration for large-scale deployments.

AV1's Growing Momentum Despite Hardware Limitations

Meta has aggressively started distributing AV1 streams for software-only playback and has co-created an open source project called VCAT (Video Codec Acid Test) to benchmark mobile devices. (Software Decoding and the Future of Mobile Video) This aggressive push demonstrates how major platforms are willing to accept the computational overhead of software decoding to achieve better compression ratios.

The strategic advantage of AV1 lies not just in its royalty-free status, but in its growing ecosystem support. While hardware decode support remains limited, the software implementations have matured significantly, making it a viable option for services willing to invest in client-side processing power.

HEVC's Continued Relevance

Despite being overshadowed by newer codecs, HEVC maintains its position as the workhorse of modern streaming. Its widespread hardware support across devices makes it the safe choice for services prioritizing compatibility over cutting-edge compression efficiency. For Hulu's diverse device ecosystem, HEVC provides the reliability needed to serve content across everything from smart TVs to mobile devices without compatibility concerns.

Hulu's Current Codec Strategy and Device Ecosystem

Understanding Hulu's Technical Infrastructure

Hulu provides users with the right to opt out of targeted advertising, selling, or sharing of personal information, indicating a sophisticated data management infrastructure that extends to content delivery optimization. (Help Center Article Detail) This infrastructure complexity suggests that codec decisions must account for not just technical performance, but also data privacy and user experience considerations.

The upcoming consolidation of Hulu into Disney's broader streaming ecosystem adds urgency to codec standardization decisions. Disney is planning to discontinue the standalone Hulu app in 2026, although the Hulu brand will continue to exist. (Standalone Hulu app will disappear from devices in 2026) This transition period presents both challenges and opportunities for codec optimization.

Device Compatibility Considerations

Streaming services must balance compression efficiency with device compatibility. The reality is that premium compression comes with computational costs that not all devices can handle gracefully. This is where AI-powered preprocessing solutions can provide immediate value by optimizing content for existing codecs rather than waiting for next-generation codec adoption. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

CDN Cost Optimization: The Hidden Driver of Codec Decisions

The Economics of Bandwidth

Content Delivery Network (CDN) costs can be reduced without compromising website speed and reliability through strategic codec selection and optimization. (Optimize CDN Costs: Proven Techniques for Big Savings) For streaming services handling petabytes of data monthly, even small percentage improvements in compression efficiency translate to substantial cost savings.

CDN providers use unique pricing models such as Pay-As-You-Go, Committed Contracts, Tiered Pricing, and Location-Based Pricing. (Optimize CDN Costs: Proven Techniques for Big Savings) Understanding these models is crucial for streaming services evaluating the ROI of codec upgrades versus alternative optimization strategies.

Telco-CDN Efficiency Challenges

Video services are increasingly shifting towards IP-based platforms, leading to a surge in IP traffic and a significant challenge for network operators. (Offloading in Telco-CDNs: Maximizing Efficiency) This shift puts additional pressure on streaming services to optimize their content delivery strategies, making codec efficiency a critical component of infrastructure planning.

Network operators invest in Telco-CDNs to handle growing traffic, but finding the right level of infrastructure can be difficult. Overcommitting can be costly, while under-committing leads to insufficient capacity. (Offloading in Telco-CDNs: Maximizing Efficiency) This infrastructure challenge creates opportunities for streaming services that can reduce their bandwidth requirements through better compression or preprocessing optimization.

AI-Powered Interim Solutions: Bridging the Codec Gap

The Role of Preprocessing in Bandwidth Optimization

While the industry debates the merits of VVC versus AV1, AI-powered preprocessing solutions offer immediate bandwidth reduction benefits that work with existing codec infrastructure. These solutions can reduce video bandwidth requirements by 22% or more while boosting perceptual quality, providing a codec-agnostic approach to optimization. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The advantage of preprocessing solutions lies in their ability to slip in front of any encoder—H.264, HEVC, AV1, AV2 or custom—so streamers can eliminate buffering and shrink CDN costs without changing their existing workflows. (Understanding Bandwidth Reduction for Streaming with AI Video Codec) This approach allows services like Hulu to achieve immediate cost savings while evaluating longer-term codec migration strategies.

Addressing AI-Generated Content Challenges

The rise of AI-generated video content presents unique compression challenges. AI video content often contains artifacts and quality issues that traditional codecs struggle to handle efficiently. (Midjourney AI Video on Social Media: Fixing AI Video Quality) As more platforms incorporate AI-generated content, preprocessing solutions that can clean up these artifacts before encoding become increasingly valuable.

Benchmarked solutions using Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set provide verified performance metrics via VMAF/SSIM metrics and golden-eye subjective studies. (Understanding Bandwidth Reduction for Streaming with AI Video Codec) This comprehensive testing approach ensures that optimization solutions work across diverse content types and quality standards.

A Phased Migration Strategy for Hulu

Phase 1: Immediate AI Preprocessing Implementation

The most practical first step for Hulu involves implementing AI-powered preprocessing solutions that can work with their existing H.264 and HEVC infrastructure. This approach provides immediate bandwidth savings without requiring device compatibility testing or codec licensing negotiations. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Key implementation steps include:

Device telemetry tagging: Implement comprehensive device capability detection to optimize preprocessing parameters for different device classes
Test-asset selection: Choose representative content samples across genres, resolutions, and complexity levels for preprocessing validation
KPI target establishment: Set measurable goals for bandwidth reduction, quality maintenance, and user experience metrics

Phase 2: Selective AV1 Deployment

Once preprocessing optimization is established, Hulu can begin selective AV1 deployment for devices and scenarios where software decoding is acceptable. This phase should focus on:

High-end mobile devices with sufficient processing power
Connected TV platforms with dedicated decoding capabilities
Premium content where quality improvements justify computational overhead

The software-only playback approach pioneered by Meta demonstrates the viability of this strategy, particularly for services willing to invest in client-side optimization. (Software Decoding and the Future of Mobile Video)

Phase 3: VVC Evaluation for 8K Content

VVC's superior compression efficiency makes it particularly attractive for 8K content delivery, where bandwidth requirements would otherwise be prohibitive. However, this deployment should be limited to:

Premium 8K content where quality justifies the complexity
Devices with confirmed VVC hardware support (as it becomes available)
Selective tile-based encoding for specific content regions

VVC's improved quality and efficiency have led to several early deployments and efficient software-only playback on mobile devices, but these remain experimental rather than production-scale implementations. (The State of VVC Adoption and Implementation)

Technical Implementation Considerations

Device Capability Detection and Adaptive Streaming

Successful codec migration requires sophisticated device capability detection that goes beyond simple codec support flags. Modern adaptive streaming systems must consider:

Processing power available for software decoding
Battery life implications of computational overhead
Network conditions and bandwidth availability
User preferences and quality settings

This complexity is where AI-powered solutions can provide additional value by optimizing content preprocessing based on detected device capabilities and network conditions. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Quality Assurance and Monitoring

Implementing new codec strategies requires comprehensive quality monitoring systems that can detect degradation across diverse content types and viewing conditions. The challenge extends beyond traditional metrics to include:

Perceptual quality assessment across different content genres
Playback performance monitoring across device types
User engagement correlation with quality changes
Cost-benefit analysis of compression versus quality trade-offs

Solutions that have been verified via VMAF/SSIM metrics and golden-eye subjective studies provide the reliability needed for production deployments. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Industry Partnerships and Ecosystem Development

The Importance of Collaborative Development

The streaming industry's codec evolution benefits from collaborative development approaches that avoid fragmentation. Meta's co-creation of the VCAT (Video Codec Acid Test) project demonstrates how industry leaders can work together to establish benchmarking standards that benefit the entire ecosystem. (Software Decoding and the Future of Mobile Video)

This collaborative approach extends to AI-powered optimization solutions, where partnerships with cloud providers and technology accelerators can provide the infrastructure and expertise needed for large-scale deployment. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Leveraging Cloud Infrastructure for Optimization

Cloud-based preprocessing and optimization solutions offer streaming services the flexibility to implement advanced compression techniques without significant infrastructure investment. These solutions can be integrated into existing workflows through SDK/API implementations that maintain compatibility with current encoding pipelines. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Measuring Success: KPIs and Performance Metrics

Establishing Baseline Measurements

Before implementing any codec migration strategy, streaming services must establish comprehensive baseline measurements that capture:

Current bandwidth utilization across content types and device categories
Quality metrics using standardized assessment tools
User engagement and satisfaction scores
CDN costs and infrastructure utilization
Device compatibility and playback success rates

Defining Success Criteria for Each Migration Phase

Each phase of codec migration should have specific, measurable success criteria:

Phase 1 (AI Preprocessing):

20%+ reduction in bandwidth requirements without quality degradation
Maintained or improved user engagement metrics
Reduced CDN costs with ROI achievement within 6 months
Zero increase in playback failures or compatibility issues

Phase 2 (AV1 Deployment):

Successful software decoding on target device categories
Additional 15-25% bandwidth reduction over preprocessing alone
Maintained battery life performance on mobile devices
Positive user experience metrics for quality-sensitive content

Phase 3 (VVC Evaluation):

Demonstrated 8K content delivery feasibility
Cost-effective compression for premium content tiers
Hardware support validation as devices become available
Scalable implementation pathway for broader deployment

Future-Proofing Streaming Infrastructure

Preparing for Next-Generation Content

As streaming services prepare for 8K content, HDR expansion, and immersive video formats, codec strategy becomes increasingly critical. The bandwidth requirements for these advanced formats make compression efficiency not just a cost optimization but a technical necessity for widespread deployment.

AI-powered preprocessing solutions provide a bridge technology that can optimize content for any target codec, ensuring that infrastructure investments remain valuable as codec standards evolve. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Maintaining Competitive Advantage

In an increasingly competitive streaming market, technical differentiation through superior compression and quality delivery can provide significant advantages. Services that can deliver higher quality content at lower bandwidth costs can offer better user experiences while maintaining healthier profit margins.

The key is implementing solutions that provide immediate benefits while maintaining flexibility for future codec adoption. This approach allows services to optimize their current infrastructure while preparing for next-generation technologies. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Conclusion: A Pragmatic Path Forward

Hulu's codec roadmap should prioritize practical solutions that deliver immediate value while preparing for future technologies. The evidence suggests that VVC adoption will be gradual, limited initially to specific use cases like 8K content where its superior compression efficiency justifies the implementation complexity.

The most pragmatic approach involves a three-phase strategy: immediate implementation of AI-powered preprocessing for existing codecs, selective AV1 deployment where software decoding is viable, and careful VVC evaluation for premium content applications. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

This strategy acknowledges the reality that codec migration is not just a technical decision but a business optimization challenge that must balance compression efficiency, device compatibility, licensing costs, and user experience. By implementing codec-agnostic optimization solutions first, streaming services can achieve immediate bandwidth savings while maintaining the flexibility to adopt new codecs as the ecosystem matures. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The streaming industry's future lies not in choosing a single "winning" codec, but in developing intelligent, adaptive systems that can optimize content delivery across multiple codecs and device capabilities. Services that embrace this flexibility while implementing immediate optimization solutions will be best positioned to navigate the evolving codec landscape successfully.

Frequently Asked Questions

What is VVC (H.266) and how does it compare to HEVC for streaming?

VVC (Versatile Video Coding) is a next-generation codec that offers up to 50% bitrate reduction over HEVC with 40% quality improvement. However, VVC currently has zero hardware decode support on mobile devices as of 2025, while HEVC enjoys widespread adoption. Despite this limitation, VVC's architecture provides only 1.6x decode complexity versus HEVC, making software-only playback feasible on modern devices.

Why might Hulu delay VVC adoption despite its efficiency benefits?

Hulu faces several barriers to VVC adoption including virtually nonexistent hardware support, complex royalty structures, and the upcoming discontinuation of the standalone Hulu app in 2026. With Disney planning to integrate Hulu into their broader ecosystem, the timing for major codec transitions becomes more strategic. Additionally, AV1 hardware decode on mobile devices remains stuck in the mid-to-low teens, suggesting similar challenges for VVC deployment.

How can AI preprocessing help reduce streaming bandwidth costs before VVC adoption?

AI preprocessing techniques can provide immediate bandwidth savings while streaming platforms wait for next-generation codec adoption. These solutions optimize video content before encoding, reducing file sizes without compromising quality. AI-powered bandwidth reduction for streaming represents a practical interim solution that can deliver cost savings and improved performance without requiring new hardware support or complex licensing negotiations.

What role does software decoding play in the future of mobile video streaming?

Software decoding is becoming increasingly important as hardware support lags behind codec innovation. Meta has aggressively started distributing AV1 streams for software-only playback and co-created VCAT (Video Codec Acid Test) to benchmark mobile devices. Some VVC IP owners like Kwai, ByteDance, and Tencent are already deploying VVC with software decode, demonstrating that efficient software-only playback is viable for modern codecs.

How do CDN costs factor into codec migration decisions for streaming services?

CDN costs significantly impact codec migration strategies as different pricing models affect bandwidth economics. CDN providers use Pay-As-You-Go, Committed Contracts, Tiered Pricing, and Location-Based Pricing models. More efficient codecs like VVC can reduce bandwidth usage by up to 50%, potentially leading to substantial CDN cost savings. However, the deployment complexity and hardware support requirements must be weighed against these potential savings.

What is the current state of VVC patent licensing and royalty structures?

Access Advance LLC announced that both HEVC Advance and VVC Advance Patent Pools will maintain current royalty rates and caps for licensees who sign before December 31, 2025. An incentive aligns royalty caps for the Multi-Codec Bridging Agreement (MCBA) and VVC Advance Patent Pool for qualifying licensees. These licensing structures create deployment barriers that streaming services must navigate when planning codec migrations.

Sources

Will Hulu Jump to VVC (H.266) Before 8K? A 2025 Codec Roadmap and Interim AI Savings

Introduction

The streaming industry stands at a critical crossroads in 2025. While VVC (H.266) promises up to 50% bitrate reduction over HEVC, hardware support remains virtually nonexistent and royalty structures create deployment barriers. (The State of VVC Adoption and Implementation) Meanwhile, AV1 hardware decode on mobile devices is stuck in the mid-to-low teens as of 2025, and VVC is at zero. (Software Decoding and the Future of Mobile Video)

For streaming giants like Hulu, which faces the discontinuation of its standalone app in 2026 as Disney consolidates its streaming strategy, codec decisions carry enormous weight. (Standalone Hulu app will disappear from devices in 2026) The question isn't just about technical superiority—it's about balancing compression efficiency, device compatibility, and cost optimization while maintaining quality standards that keep subscribers engaged.

This analysis examines Hulu's potential codec migration path, comparing VVC, AV1, and HEVC using 2025 peer-reviewed BD-rate data, while exploring how AI-powered preprocessing solutions can deliver immediate bandwidth savings without disrupting existing workflows. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The Current Codec Landscape: Performance vs. Practicality

VVC's Technical Promise and Real-World Hurdles

VVC's architecture and new coding tools have contributed to a quality improvement of 40% over HEVC, with only a 1.6x decode complexity versus HEVC. (The State of VVC Adoption and Implementation) This represents a significant leap in compression efficiency, potentially revolutionizing how streaming services manage bandwidth costs and quality delivery.

However, the reality of deployment tells a different story. Some VVC IP owners like Kwai, ByteDance, and Tencent are deploying VVC with software decode, but these implementations remain limited to specific use cases and controlled environments. (Software Decoding and the Future of Mobile Video)

The royalty landscape adds another layer of complexity. Access Advance LLC announced that both HEVC Advance and VVC Advance Patent Pools opted to maintain their current royalty rates and caps for Licensees who sign before December 31, 2025. (Access Advance Announces HEVC Advance and VVC Advance Pricing through 2030) While this provides some pricing certainty, the cumulative cost of licensing multiple patent pools remains a significant consideration for large-scale deployments.

AV1's Growing Momentum Despite Hardware Limitations

Meta has aggressively started distributing AV1 streams for software-only playback and has co-created an open source project called VCAT (Video Codec Acid Test) to benchmark mobile devices. (Software Decoding and the Future of Mobile Video) This aggressive push demonstrates how major platforms are willing to accept the computational overhead of software decoding to achieve better compression ratios.

The strategic advantage of AV1 lies not just in its royalty-free status, but in its growing ecosystem support. While hardware decode support remains limited, the software implementations have matured significantly, making it a viable option for services willing to invest in client-side processing power.

HEVC's Continued Relevance

Despite being overshadowed by newer codecs, HEVC maintains its position as the workhorse of modern streaming. Its widespread hardware support across devices makes it the safe choice for services prioritizing compatibility over cutting-edge compression efficiency. For Hulu's diverse device ecosystem, HEVC provides the reliability needed to serve content across everything from smart TVs to mobile devices without compatibility concerns.

Hulu's Current Codec Strategy and Device Ecosystem

Understanding Hulu's Technical Infrastructure

Hulu provides users with the right to opt out of targeted advertising, selling, or sharing of personal information, indicating a sophisticated data management infrastructure that extends to content delivery optimization. (Help Center Article Detail) This infrastructure complexity suggests that codec decisions must account for not just technical performance, but also data privacy and user experience considerations.

The upcoming consolidation of Hulu into Disney's broader streaming ecosystem adds urgency to codec standardization decisions. Disney is planning to discontinue the standalone Hulu app in 2026, although the Hulu brand will continue to exist. (Standalone Hulu app will disappear from devices in 2026) This transition period presents both challenges and opportunities for codec optimization.

Device Compatibility Considerations

Streaming services must balance compression efficiency with device compatibility. The reality is that premium compression comes with computational costs that not all devices can handle gracefully. This is where AI-powered preprocessing solutions can provide immediate value by optimizing content for existing codecs rather than waiting for next-generation codec adoption. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

CDN Cost Optimization: The Hidden Driver of Codec Decisions

The Economics of Bandwidth

Content Delivery Network (CDN) costs can be reduced without compromising website speed and reliability through strategic codec selection and optimization. (Optimize CDN Costs: Proven Techniques for Big Savings) For streaming services handling petabytes of data monthly, even small percentage improvements in compression efficiency translate to substantial cost savings.

CDN providers use unique pricing models such as Pay-As-You-Go, Committed Contracts, Tiered Pricing, and Location-Based Pricing. (Optimize CDN Costs: Proven Techniques for Big Savings) Understanding these models is crucial for streaming services evaluating the ROI of codec upgrades versus alternative optimization strategies.

Telco-CDN Efficiency Challenges

Video services are increasingly shifting towards IP-based platforms, leading to a surge in IP traffic and a significant challenge for network operators. (Offloading in Telco-CDNs: Maximizing Efficiency) This shift puts additional pressure on streaming services to optimize their content delivery strategies, making codec efficiency a critical component of infrastructure planning.

Network operators invest in Telco-CDNs to handle growing traffic, but finding the right level of infrastructure can be difficult. Overcommitting can be costly, while under-committing leads to insufficient capacity. (Offloading in Telco-CDNs: Maximizing Efficiency) This infrastructure challenge creates opportunities for streaming services that can reduce their bandwidth requirements through better compression or preprocessing optimization.

AI-Powered Interim Solutions: Bridging the Codec Gap

The Role of Preprocessing in Bandwidth Optimization

While the industry debates the merits of VVC versus AV1, AI-powered preprocessing solutions offer immediate bandwidth reduction benefits that work with existing codec infrastructure. These solutions can reduce video bandwidth requirements by 22% or more while boosting perceptual quality, providing a codec-agnostic approach to optimization. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The advantage of preprocessing solutions lies in their ability to slip in front of any encoder—H.264, HEVC, AV1, AV2 or custom—so streamers can eliminate buffering and shrink CDN costs without changing their existing workflows. (Understanding Bandwidth Reduction for Streaming with AI Video Codec) This approach allows services like Hulu to achieve immediate cost savings while evaluating longer-term codec migration strategies.

Addressing AI-Generated Content Challenges

The rise of AI-generated video content presents unique compression challenges. AI video content often contains artifacts and quality issues that traditional codecs struggle to handle efficiently. (Midjourney AI Video on Social Media: Fixing AI Video Quality) As more platforms incorporate AI-generated content, preprocessing solutions that can clean up these artifacts before encoding become increasingly valuable.

Benchmarked solutions using Netflix Open Content, YouTube UGC, and the OpenVid-1M GenAI video set provide verified performance metrics via VMAF/SSIM metrics and golden-eye subjective studies. (Understanding Bandwidth Reduction for Streaming with AI Video Codec) This comprehensive testing approach ensures that optimization solutions work across diverse content types and quality standards.

A Phased Migration Strategy for Hulu

Phase 1: Immediate AI Preprocessing Implementation

The most practical first step for Hulu involves implementing AI-powered preprocessing solutions that can work with their existing H.264 and HEVC infrastructure. This approach provides immediate bandwidth savings without requiring device compatibility testing or codec licensing negotiations. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Key implementation steps include:

Device telemetry tagging: Implement comprehensive device capability detection to optimize preprocessing parameters for different device classes
Test-asset selection: Choose representative content samples across genres, resolutions, and complexity levels for preprocessing validation
KPI target establishment: Set measurable goals for bandwidth reduction, quality maintenance, and user experience metrics

Phase 2: Selective AV1 Deployment

Once preprocessing optimization is established, Hulu can begin selective AV1 deployment for devices and scenarios where software decoding is acceptable. This phase should focus on:

High-end mobile devices with sufficient processing power
Connected TV platforms with dedicated decoding capabilities
Premium content where quality improvements justify computational overhead

The software-only playback approach pioneered by Meta demonstrates the viability of this strategy, particularly for services willing to invest in client-side optimization. (Software Decoding and the Future of Mobile Video)

Phase 3: VVC Evaluation for 8K Content

VVC's superior compression efficiency makes it particularly attractive for 8K content delivery, where bandwidth requirements would otherwise be prohibitive. However, this deployment should be limited to:

Premium 8K content where quality justifies the complexity
Devices with confirmed VVC hardware support (as it becomes available)
Selective tile-based encoding for specific content regions

VVC's improved quality and efficiency have led to several early deployments and efficient software-only playback on mobile devices, but these remain experimental rather than production-scale implementations. (The State of VVC Adoption and Implementation)

Technical Implementation Considerations

Device Capability Detection and Adaptive Streaming

Successful codec migration requires sophisticated device capability detection that goes beyond simple codec support flags. Modern adaptive streaming systems must consider:

Processing power available for software decoding
Battery life implications of computational overhead
Network conditions and bandwidth availability
User preferences and quality settings

This complexity is where AI-powered solutions can provide additional value by optimizing content preprocessing based on detected device capabilities and network conditions. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Quality Assurance and Monitoring

Implementing new codec strategies requires comprehensive quality monitoring systems that can detect degradation across diverse content types and viewing conditions. The challenge extends beyond traditional metrics to include:

Perceptual quality assessment across different content genres
Playback performance monitoring across device types
User engagement correlation with quality changes
Cost-benefit analysis of compression versus quality trade-offs

Solutions that have been verified via VMAF/SSIM metrics and golden-eye subjective studies provide the reliability needed for production deployments. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Industry Partnerships and Ecosystem Development

The Importance of Collaborative Development

The streaming industry's codec evolution benefits from collaborative development approaches that avoid fragmentation. Meta's co-creation of the VCAT (Video Codec Acid Test) project demonstrates how industry leaders can work together to establish benchmarking standards that benefit the entire ecosystem. (Software Decoding and the Future of Mobile Video)

This collaborative approach extends to AI-powered optimization solutions, where partnerships with cloud providers and technology accelerators can provide the infrastructure and expertise needed for large-scale deployment. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Leveraging Cloud Infrastructure for Optimization

Cloud-based preprocessing and optimization solutions offer streaming services the flexibility to implement advanced compression techniques without significant infrastructure investment. These solutions can be integrated into existing workflows through SDK/API implementations that maintain compatibility with current encoding pipelines. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Measuring Success: KPIs and Performance Metrics

Establishing Baseline Measurements

Before implementing any codec migration strategy, streaming services must establish comprehensive baseline measurements that capture:

Current bandwidth utilization across content types and device categories
Quality metrics using standardized assessment tools
User engagement and satisfaction scores
CDN costs and infrastructure utilization
Device compatibility and playback success rates

Defining Success Criteria for Each Migration Phase

Each phase of codec migration should have specific, measurable success criteria:

Phase 1 (AI Preprocessing):

20%+ reduction in bandwidth requirements without quality degradation
Maintained or improved user engagement metrics
Reduced CDN costs with ROI achievement within 6 months
Zero increase in playback failures or compatibility issues

Phase 2 (AV1 Deployment):

Successful software decoding on target device categories
Additional 15-25% bandwidth reduction over preprocessing alone
Maintained battery life performance on mobile devices
Positive user experience metrics for quality-sensitive content

Phase 3 (VVC Evaluation):

Demonstrated 8K content delivery feasibility
Cost-effective compression for premium content tiers
Hardware support validation as devices become available
Scalable implementation pathway for broader deployment

Future-Proofing Streaming Infrastructure

Preparing for Next-Generation Content

As streaming services prepare for 8K content, HDR expansion, and immersive video formats, codec strategy becomes increasingly critical. The bandwidth requirements for these advanced formats make compression efficiency not just a cost optimization but a technical necessity for widespread deployment.

AI-powered preprocessing solutions provide a bridge technology that can optimize content for any target codec, ensuring that infrastructure investments remain valuable as codec standards evolve. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Maintaining Competitive Advantage

In an increasingly competitive streaming market, technical differentiation through superior compression and quality delivery can provide significant advantages. Services that can deliver higher quality content at lower bandwidth costs can offer better user experiences while maintaining healthier profit margins.

The key is implementing solutions that provide immediate benefits while maintaining flexibility for future codec adoption. This approach allows services to optimize their current infrastructure while preparing for next-generation technologies. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

Conclusion: A Pragmatic Path Forward

Hulu's codec roadmap should prioritize practical solutions that deliver immediate value while preparing for future technologies. The evidence suggests that VVC adoption will be gradual, limited initially to specific use cases like 8K content where its superior compression efficiency justifies the implementation complexity.

The most pragmatic approach involves a three-phase strategy: immediate implementation of AI-powered preprocessing for existing codecs, selective AV1 deployment where software decoding is viable, and careful VVC evaluation for premium content applications. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

This strategy acknowledges the reality that codec migration is not just a technical decision but a business optimization challenge that must balance compression efficiency, device compatibility, licensing costs, and user experience. By implementing codec-agnostic optimization solutions first, streaming services can achieve immediate bandwidth savings while maintaining the flexibility to adopt new codecs as the ecosystem matures. (Understanding Bandwidth Reduction for Streaming with AI Video Codec)

The streaming industry's future lies not in choosing a single "winning" codec, but in developing intelligent, adaptive systems that can optimize content delivery across multiple codecs and device capabilities. Services that embrace this flexibility while implementing immediate optimization solutions will be best positioned to navigate the evolving codec landscape successfully.

Frequently Asked Questions

What is VVC (H.266) and how does it compare to HEVC for streaming?

VVC (Versatile Video Coding) is a next-generation codec that offers up to 50% bitrate reduction over HEVC with 40% quality improvement. However, VVC currently has zero hardware decode support on mobile devices as of 2025, while HEVC enjoys widespread adoption. Despite this limitation, VVC's architecture provides only 1.6x decode complexity versus HEVC, making software-only playback feasible on modern devices.

Why might Hulu delay VVC adoption despite its efficiency benefits?

Hulu faces several barriers to VVC adoption including virtually nonexistent hardware support, complex royalty structures, and the upcoming discontinuation of the standalone Hulu app in 2026. With Disney planning to integrate Hulu into their broader ecosystem, the timing for major codec transitions becomes more strategic. Additionally, AV1 hardware decode on mobile devices remains stuck in the mid-to-low teens, suggesting similar challenges for VVC deployment.

How can AI preprocessing help reduce streaming bandwidth costs before VVC adoption?

AI preprocessing techniques can provide immediate bandwidth savings while streaming platforms wait for next-generation codec adoption. These solutions optimize video content before encoding, reducing file sizes without compromising quality. AI-powered bandwidth reduction for streaming represents a practical interim solution that can deliver cost savings and improved performance without requiring new hardware support or complex licensing negotiations.

What role does software decoding play in the future of mobile video streaming?

Software decoding is becoming increasingly important as hardware support lags behind codec innovation. Meta has aggressively started distributing AV1 streams for software-only playback and co-created VCAT (Video Codec Acid Test) to benchmark mobile devices. Some VVC IP owners like Kwai, ByteDance, and Tencent are already deploying VVC with software decode, demonstrating that efficient software-only playback is viable for modern codecs.

How do CDN costs factor into codec migration decisions for streaming services?

CDN costs significantly impact codec migration strategies as different pricing models affect bandwidth economics. CDN providers use Pay-As-You-Go, Committed Contracts, Tiered Pricing, and Location-Based Pricing models. More efficient codecs like VVC can reduce bandwidth usage by up to 50%, potentially leading to substantial CDN cost savings. However, the deployment complexity and hardware support requirements must be weighed against these potential savings.

What is the current state of VVC patent licensing and royalty structures?

Access Advance LLC announced that both HEVC Advance and VVC Advance Patent Pools will maintain current royalty rates and caps for licensees who sign before December 31, 2025. An incentive aligns royalty caps for the Multi-Codec Bridging Agreement (MCBA) and VVC Advance Patent Pool for qualifying licensees. These licensing structures create deployment barriers that streaming services must navigate when planning codec migrations.