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How to Integrate SimaBit into Magnifi’s Real-Time Highlight Workflow for College Football Streams on AWS MediaLive (Q4 2025 Guide)
How to Integrate SimaBit into Magnifi's Real-Time Highlight Workflow for College Football Streams on AWS MediaLive (Q4 2025 Guide)
College-football productions that Integrate SimaBit with Magnifi cut bandwidth bills yet still publish touchdown clips in seconds. This guide walks DevOps teams through the exact hand-offs on AWS so you can replicate the workflow before bowl season.
Why Pair SimaBit and Magnifi for Live College Football?
Live college football streaming demands both efficiency and speed. With Mountain West championship viewership peaking at 3.505 million viewers, broadcasters need solutions that handle scale without breaking budgets.
SimaBit's AI preprocessing engine reduces bandwidth by 22% or more while maintaining broadcast quality. When combined with Magnifi's automated highlights platform, which uses machine learning to identify key moments unique to each sport, you get a workflow that slashes both bandwidth costs and production time.
The pairing makes particular sense for college football. Games generate massive amounts of data - touchdowns, field goals, turnovers - that need instant clipping and distribution. SimaBit handles the bandwidth optimization upfront, while Magnifi's AI watches the video stream and creates metadata on the fly, eliminating dependency on external stats feeds.
End-to-End Architecture: RTMP ➝ SimaBit ➝ MediaLive ➝ Magnifi
The signal flow follows a straightforward path. Your stadium camera feeds push RTMP to SimaBit's preprocessing layer. AWS Elemental MediaConnect provides the reliable ingestion backbone, making it easy for broadcasters to reliably ingest live video into the AWS Cloud.
SimaBit processes the incoming RTMP stream as a transparent filter. The AI engine analyzes each frame, optimizing bit allocation before passing the stream to MediaLive. MediaLive ingests and transcodes your content into adaptive bitrate HLS streams.
From there, Magnifi's platform generates automated highlight data using custom cues. The AI identifies touchdowns, interceptions, and other key plays without waiting for external data feeds. This architecture keeps latency minimal while maximizing efficiency at every stage.
Step 1 - Ingest the Stadium RTMP Feed and Pre-Filter with SimaBit
Start by configuring your RTMP encoder at the stadium. SimaBit installs in front of any encoder - H.264, HEVC, AV1, or custom - preserving your existing toolchain.
For GPU sizing, SiMa.ai's MLSoC hardware provides optimized performance for AI workloads. Select instances based on your stream count and resolution requirements. A typical college football production handling 4K feeds benefits from GPU-accelerated instances.
MediaLive supports four input types including RTP_PUSH and RTMP_PUSH. Configure your SimaBit output to match MediaLive's expected input format. The preprocessing happens transparently - your downstream systems see a standard RTMP stream, just optimized for bandwidth.
Step 2 - Hand Off to AWS MediaLive for Multi-Bitrate HLS
MediaLive automatically configures encoding for adaptive bitrate streaming via HLS. Create your channel with the appropriate encoding profiles for your target devices.
SimaBit's preprocessing delivers 22-35% additional bandwidth savings when combined with modern codecs. This means your ABR ladder can maintain higher quality at lower bitrates, improving the viewing experience on congested networks.
Configure your MediaLive outputs to write HLS segments to S3. ALPHAS trials show 21% latency improvements during multi-game slates, ensuring your highlights stay timely even during peak Saturday traffic.
Step 3 - Real-Time Highlight Detection & Publishing with Magnifi
Connect MediaLive's HLS output URLs to Magnifi's ingest endpoints. As Grabyo's partnership demonstrates, "Automated highlight data is generated by Magnifi's platform using custom cues, which are created using machine learning to identify key moments unique to each sport."
Magnifi's AI engine processes the video independently. "Our AI does not wait for external stats. It watches the video, understands the action, and creates metadata on the fly." This eliminates dependencies on third-party data feeds that can lag or fail during critical moments.
The platform can save 80% of production costs while operating across multiple events simultaneously. Users can view nine live streams with automated clips and compilations generated in real-time.
Once Magnifi identifies a highlight - say a game-winning touchdown - it triggers immediate clip creation. These clips route directly to your OTT platform and social channels, getting content to fans within seconds of the play.
Bandwidth, Egress & Latency: The Business Case
College football Saturdays push infrastructure to its limits. The Mountain West championship peaked at 3.505 million concurrent viewers during 2024.
With SimaBit's verified 22% bandwidth reduction, a typical Saturday streaming 10TB of video saves 2.2TB in CDN transfer. AWS egress costs add up quickly - that 2.2TB savings translates to real dollars every game day.
Latency remains unchanged or even improves. Independent ALPHAS testing showed up to 21% lower end-to-end latency during multi-game scenarios. Your highlight clips reach social media just as fast, but at significantly lower cost.
Terraform Blueprint & IaC Tips
Deploy your infrastructure using Terraform for repeatability. Start with authentication credentials for MediaConnect - you'll need Access key, Secret access key, and Region information.
AWS uses managed and serverless services to minimize environmental impact while maximizing availability. Your Terraform should provision MediaLive channels, S3 buckets for HLS segments, and CloudFront distributions.
For SimaBit GPU instances, Edgematic offers seamless deployment through low-code pipelines. Size your instances based on concurrent stream count - a g4dn.2xlarge handles typical single-game loads, while multi-game Saturdays may require g4dn.12xlarge or larger.
Key Takeaways
Integrating SimaBit with Magnifi transforms college football streaming economics. SimaBit's preprocessing approach minimizes implementation risk - you can test and deploy incrementally while maintaining existing infrastructure.
The combined solution delivers measurable benefits: "SimaBit + Any Codec| 22-35% additional| Enhanced| Low (preprocessing)" bandwidth reduction, 80% production cost savings from Magnifi, and instant highlight distribution to maximize fan engagement. With bowl season approaching, now is the time to implement this workflow and capture the efficiency gains for your biggest games.
For organizations looking to optimize their live sports streaming infrastructure, Sima Labs offers the proven AI preprocessing technology that makes this entire workflow possible. SimaBit's seamless integration with AWS MediaLive and compatibility with Magnifi's highlight engine provides the foundation for next-generation sports broadcasting at scale.
Frequently Asked Questions
How does the RTMP → SimaBit → MediaLive → Magnifi workflow operate end-to-end?
Stadium cameras send RTMP to SimaBit for AI preprocessing, which optimizes bits without changing your encoder settings. The filtered stream is handed to AWS MediaLive for ABR HLS packaging. Magnifi ingests the HLS outputs, detects touchdowns and other key plays, and publishes clips to OTT and social in near real time.
Will SimaBit reduce my AWS egress costs for college football Saturdays?
Yes. SimaBit’s verified benchmarks show around 22% bandwidth reduction; a 10TB Saturday can save about 2.2TB in CDN transfer, translating to meaningful egress savings. See Sima Labs’ evaluation for details: https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc.
Does SimaBit add latency or affect Magnifi’s real-time clip detection?
No. The preprocessing step is transparent, and independent testing cited in the guide shows latency unchanged or improved, with up to 21% lower end-to-end latency during multi-game slates. Magnifi’s clip-detection speed remains unaffected.
What AWS instance types should I use for SimaBit preprocessing?
Size GPU instances to stream count and resolution. A g4dn.2xlarge typically handles a single 4K game, while multi-game Saturdays often require g4dn.12xlarge or larger. Monitor utilization and scale horizontally for parallel feeds.
How do I connect AWS MediaLive outputs to Magnifi?
Configure MediaLive to output HLS to S3 and expose the playlist URLs. Provide those HLS endpoints to Magnifi’s ingest; its ML-based cues detect touchdowns, interceptions, and other moments, triggering immediate clip creation for social and OTT.
Can I keep my existing encoder and ABR ladder when adding SimaBit?
Yes. SimaBit installs in front of any encoder (H.264, HEVC, AV1, or custom) and outputs a standard RTMP feed to MediaLive. Downstream systems see a normal stream, but at materially lower bitrates for the same perceived quality.
Sources
How to Integrate SimaBit into Magnifi's Real-Time Highlight Workflow for College Football Streams on AWS MediaLive (Q4 2025 Guide)
College-football productions that Integrate SimaBit with Magnifi cut bandwidth bills yet still publish touchdown clips in seconds. This guide walks DevOps teams through the exact hand-offs on AWS so you can replicate the workflow before bowl season.
Why Pair SimaBit and Magnifi for Live College Football?
Live college football streaming demands both efficiency and speed. With Mountain West championship viewership peaking at 3.505 million viewers, broadcasters need solutions that handle scale without breaking budgets.
SimaBit's AI preprocessing engine reduces bandwidth by 22% or more while maintaining broadcast quality. When combined with Magnifi's automated highlights platform, which uses machine learning to identify key moments unique to each sport, you get a workflow that slashes both bandwidth costs and production time.
The pairing makes particular sense for college football. Games generate massive amounts of data - touchdowns, field goals, turnovers - that need instant clipping and distribution. SimaBit handles the bandwidth optimization upfront, while Magnifi's AI watches the video stream and creates metadata on the fly, eliminating dependency on external stats feeds.
End-to-End Architecture: RTMP ➝ SimaBit ➝ MediaLive ➝ Magnifi
The signal flow follows a straightforward path. Your stadium camera feeds push RTMP to SimaBit's preprocessing layer. AWS Elemental MediaConnect provides the reliable ingestion backbone, making it easy for broadcasters to reliably ingest live video into the AWS Cloud.
SimaBit processes the incoming RTMP stream as a transparent filter. The AI engine analyzes each frame, optimizing bit allocation before passing the stream to MediaLive. MediaLive ingests and transcodes your content into adaptive bitrate HLS streams.
From there, Magnifi's platform generates automated highlight data using custom cues. The AI identifies touchdowns, interceptions, and other key plays without waiting for external data feeds. This architecture keeps latency minimal while maximizing efficiency at every stage.
Step 1 - Ingest the Stadium RTMP Feed and Pre-Filter with SimaBit
Start by configuring your RTMP encoder at the stadium. SimaBit installs in front of any encoder - H.264, HEVC, AV1, or custom - preserving your existing toolchain.
For GPU sizing, SiMa.ai's MLSoC hardware provides optimized performance for AI workloads. Select instances based on your stream count and resolution requirements. A typical college football production handling 4K feeds benefits from GPU-accelerated instances.
MediaLive supports four input types including RTP_PUSH and RTMP_PUSH. Configure your SimaBit output to match MediaLive's expected input format. The preprocessing happens transparently - your downstream systems see a standard RTMP stream, just optimized for bandwidth.
Step 2 - Hand Off to AWS MediaLive for Multi-Bitrate HLS
MediaLive automatically configures encoding for adaptive bitrate streaming via HLS. Create your channel with the appropriate encoding profiles for your target devices.
SimaBit's preprocessing delivers 22-35% additional bandwidth savings when combined with modern codecs. This means your ABR ladder can maintain higher quality at lower bitrates, improving the viewing experience on congested networks.
Configure your MediaLive outputs to write HLS segments to S3. ALPHAS trials show 21% latency improvements during multi-game slates, ensuring your highlights stay timely even during peak Saturday traffic.
Step 3 - Real-Time Highlight Detection & Publishing with Magnifi
Connect MediaLive's HLS output URLs to Magnifi's ingest endpoints. As Grabyo's partnership demonstrates, "Automated highlight data is generated by Magnifi's platform using custom cues, which are created using machine learning to identify key moments unique to each sport."
Magnifi's AI engine processes the video independently. "Our AI does not wait for external stats. It watches the video, understands the action, and creates metadata on the fly." This eliminates dependencies on third-party data feeds that can lag or fail during critical moments.
The platform can save 80% of production costs while operating across multiple events simultaneously. Users can view nine live streams with automated clips and compilations generated in real-time.
Once Magnifi identifies a highlight - say a game-winning touchdown - it triggers immediate clip creation. These clips route directly to your OTT platform and social channels, getting content to fans within seconds of the play.
Bandwidth, Egress & Latency: The Business Case
College football Saturdays push infrastructure to its limits. The Mountain West championship peaked at 3.505 million concurrent viewers during 2024.
With SimaBit's verified 22% bandwidth reduction, a typical Saturday streaming 10TB of video saves 2.2TB in CDN transfer. AWS egress costs add up quickly - that 2.2TB savings translates to real dollars every game day.
Latency remains unchanged or even improves. Independent ALPHAS testing showed up to 21% lower end-to-end latency during multi-game scenarios. Your highlight clips reach social media just as fast, but at significantly lower cost.
Terraform Blueprint & IaC Tips
Deploy your infrastructure using Terraform for repeatability. Start with authentication credentials for MediaConnect - you'll need Access key, Secret access key, and Region information.
AWS uses managed and serverless services to minimize environmental impact while maximizing availability. Your Terraform should provision MediaLive channels, S3 buckets for HLS segments, and CloudFront distributions.
For SimaBit GPU instances, Edgematic offers seamless deployment through low-code pipelines. Size your instances based on concurrent stream count - a g4dn.2xlarge handles typical single-game loads, while multi-game Saturdays may require g4dn.12xlarge or larger.
Key Takeaways
Integrating SimaBit with Magnifi transforms college football streaming economics. SimaBit's preprocessing approach minimizes implementation risk - you can test and deploy incrementally while maintaining existing infrastructure.
The combined solution delivers measurable benefits: "SimaBit + Any Codec| 22-35% additional| Enhanced| Low (preprocessing)" bandwidth reduction, 80% production cost savings from Magnifi, and instant highlight distribution to maximize fan engagement. With bowl season approaching, now is the time to implement this workflow and capture the efficiency gains for your biggest games.
For organizations looking to optimize their live sports streaming infrastructure, Sima Labs offers the proven AI preprocessing technology that makes this entire workflow possible. SimaBit's seamless integration with AWS MediaLive and compatibility with Magnifi's highlight engine provides the foundation for next-generation sports broadcasting at scale.
Frequently Asked Questions
How does the RTMP → SimaBit → MediaLive → Magnifi workflow operate end-to-end?
Stadium cameras send RTMP to SimaBit for AI preprocessing, which optimizes bits without changing your encoder settings. The filtered stream is handed to AWS MediaLive for ABR HLS packaging. Magnifi ingests the HLS outputs, detects touchdowns and other key plays, and publishes clips to OTT and social in near real time.
Will SimaBit reduce my AWS egress costs for college football Saturdays?
Yes. SimaBit’s verified benchmarks show around 22% bandwidth reduction; a 10TB Saturday can save about 2.2TB in CDN transfer, translating to meaningful egress savings. See Sima Labs’ evaluation for details: https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc.
Does SimaBit add latency or affect Magnifi’s real-time clip detection?
No. The preprocessing step is transparent, and independent testing cited in the guide shows latency unchanged or improved, with up to 21% lower end-to-end latency during multi-game slates. Magnifi’s clip-detection speed remains unaffected.
What AWS instance types should I use for SimaBit preprocessing?
Size GPU instances to stream count and resolution. A g4dn.2xlarge typically handles a single 4K game, while multi-game Saturdays often require g4dn.12xlarge or larger. Monitor utilization and scale horizontally for parallel feeds.
How do I connect AWS MediaLive outputs to Magnifi?
Configure MediaLive to output HLS to S3 and expose the playlist URLs. Provide those HLS endpoints to Magnifi’s ingest; its ML-based cues detect touchdowns, interceptions, and other moments, triggering immediate clip creation for social and OTT.
Can I keep my existing encoder and ABR ladder when adding SimaBit?
Yes. SimaBit installs in front of any encoder (H.264, HEVC, AV1, or custom) and outputs a standard RTMP feed to MediaLive. Downstream systems see a normal stream, but at materially lower bitrates for the same perceived quality.
Sources
How to Integrate SimaBit into Magnifi's Real-Time Highlight Workflow for College Football Streams on AWS MediaLive (Q4 2025 Guide)
College-football productions that Integrate SimaBit with Magnifi cut bandwidth bills yet still publish touchdown clips in seconds. This guide walks DevOps teams through the exact hand-offs on AWS so you can replicate the workflow before bowl season.
Why Pair SimaBit and Magnifi for Live College Football?
Live college football streaming demands both efficiency and speed. With Mountain West championship viewership peaking at 3.505 million viewers, broadcasters need solutions that handle scale without breaking budgets.
SimaBit's AI preprocessing engine reduces bandwidth by 22% or more while maintaining broadcast quality. When combined with Magnifi's automated highlights platform, which uses machine learning to identify key moments unique to each sport, you get a workflow that slashes both bandwidth costs and production time.
The pairing makes particular sense for college football. Games generate massive amounts of data - touchdowns, field goals, turnovers - that need instant clipping and distribution. SimaBit handles the bandwidth optimization upfront, while Magnifi's AI watches the video stream and creates metadata on the fly, eliminating dependency on external stats feeds.
End-to-End Architecture: RTMP ➝ SimaBit ➝ MediaLive ➝ Magnifi
The signal flow follows a straightforward path. Your stadium camera feeds push RTMP to SimaBit's preprocessing layer. AWS Elemental MediaConnect provides the reliable ingestion backbone, making it easy for broadcasters to reliably ingest live video into the AWS Cloud.
SimaBit processes the incoming RTMP stream as a transparent filter. The AI engine analyzes each frame, optimizing bit allocation before passing the stream to MediaLive. MediaLive ingests and transcodes your content into adaptive bitrate HLS streams.
From there, Magnifi's platform generates automated highlight data using custom cues. The AI identifies touchdowns, interceptions, and other key plays without waiting for external data feeds. This architecture keeps latency minimal while maximizing efficiency at every stage.
Step 1 - Ingest the Stadium RTMP Feed and Pre-Filter with SimaBit
Start by configuring your RTMP encoder at the stadium. SimaBit installs in front of any encoder - H.264, HEVC, AV1, or custom - preserving your existing toolchain.
For GPU sizing, SiMa.ai's MLSoC hardware provides optimized performance for AI workloads. Select instances based on your stream count and resolution requirements. A typical college football production handling 4K feeds benefits from GPU-accelerated instances.
MediaLive supports four input types including RTP_PUSH and RTMP_PUSH. Configure your SimaBit output to match MediaLive's expected input format. The preprocessing happens transparently - your downstream systems see a standard RTMP stream, just optimized for bandwidth.
Step 2 - Hand Off to AWS MediaLive for Multi-Bitrate HLS
MediaLive automatically configures encoding for adaptive bitrate streaming via HLS. Create your channel with the appropriate encoding profiles for your target devices.
SimaBit's preprocessing delivers 22-35% additional bandwidth savings when combined with modern codecs. This means your ABR ladder can maintain higher quality at lower bitrates, improving the viewing experience on congested networks.
Configure your MediaLive outputs to write HLS segments to S3. ALPHAS trials show 21% latency improvements during multi-game slates, ensuring your highlights stay timely even during peak Saturday traffic.
Step 3 - Real-Time Highlight Detection & Publishing with Magnifi
Connect MediaLive's HLS output URLs to Magnifi's ingest endpoints. As Grabyo's partnership demonstrates, "Automated highlight data is generated by Magnifi's platform using custom cues, which are created using machine learning to identify key moments unique to each sport."
Magnifi's AI engine processes the video independently. "Our AI does not wait for external stats. It watches the video, understands the action, and creates metadata on the fly." This eliminates dependencies on third-party data feeds that can lag or fail during critical moments.
The platform can save 80% of production costs while operating across multiple events simultaneously. Users can view nine live streams with automated clips and compilations generated in real-time.
Once Magnifi identifies a highlight - say a game-winning touchdown - it triggers immediate clip creation. These clips route directly to your OTT platform and social channels, getting content to fans within seconds of the play.
Bandwidth, Egress & Latency: The Business Case
College football Saturdays push infrastructure to its limits. The Mountain West championship peaked at 3.505 million concurrent viewers during 2024.
With SimaBit's verified 22% bandwidth reduction, a typical Saturday streaming 10TB of video saves 2.2TB in CDN transfer. AWS egress costs add up quickly - that 2.2TB savings translates to real dollars every game day.
Latency remains unchanged or even improves. Independent ALPHAS testing showed up to 21% lower end-to-end latency during multi-game scenarios. Your highlight clips reach social media just as fast, but at significantly lower cost.
Terraform Blueprint & IaC Tips
Deploy your infrastructure using Terraform for repeatability. Start with authentication credentials for MediaConnect - you'll need Access key, Secret access key, and Region information.
AWS uses managed and serverless services to minimize environmental impact while maximizing availability. Your Terraform should provision MediaLive channels, S3 buckets for HLS segments, and CloudFront distributions.
For SimaBit GPU instances, Edgematic offers seamless deployment through low-code pipelines. Size your instances based on concurrent stream count - a g4dn.2xlarge handles typical single-game loads, while multi-game Saturdays may require g4dn.12xlarge or larger.
Key Takeaways
Integrating SimaBit with Magnifi transforms college football streaming economics. SimaBit's preprocessing approach minimizes implementation risk - you can test and deploy incrementally while maintaining existing infrastructure.
The combined solution delivers measurable benefits: "SimaBit + Any Codec| 22-35% additional| Enhanced| Low (preprocessing)" bandwidth reduction, 80% production cost savings from Magnifi, and instant highlight distribution to maximize fan engagement. With bowl season approaching, now is the time to implement this workflow and capture the efficiency gains for your biggest games.
For organizations looking to optimize their live sports streaming infrastructure, Sima Labs offers the proven AI preprocessing technology that makes this entire workflow possible. SimaBit's seamless integration with AWS MediaLive and compatibility with Magnifi's highlight engine provides the foundation for next-generation sports broadcasting at scale.
Frequently Asked Questions
How does the RTMP → SimaBit → MediaLive → Magnifi workflow operate end-to-end?
Stadium cameras send RTMP to SimaBit for AI preprocessing, which optimizes bits without changing your encoder settings. The filtered stream is handed to AWS MediaLive for ABR HLS packaging. Magnifi ingests the HLS outputs, detects touchdowns and other key plays, and publishes clips to OTT and social in near real time.
Will SimaBit reduce my AWS egress costs for college football Saturdays?
Yes. SimaBit’s verified benchmarks show around 22% bandwidth reduction; a 10TB Saturday can save about 2.2TB in CDN transfer, translating to meaningful egress savings. See Sima Labs’ evaluation for details: https://www.simalabs.ai/resources/openvid-1m-genai-evaluation-ai-preprocessing-vmaf-ugc.
Does SimaBit add latency or affect Magnifi’s real-time clip detection?
No. The preprocessing step is transparent, and independent testing cited in the guide shows latency unchanged or improved, with up to 21% lower end-to-end latency during multi-game slates. Magnifi’s clip-detection speed remains unaffected.
What AWS instance types should I use for SimaBit preprocessing?
Size GPU instances to stream count and resolution. A g4dn.2xlarge typically handles a single 4K game, while multi-game Saturdays often require g4dn.12xlarge or larger. Monitor utilization and scale horizontally for parallel feeds.
How do I connect AWS MediaLive outputs to Magnifi?
Configure MediaLive to output HLS to S3 and expose the playlist URLs. Provide those HLS endpoints to Magnifi’s ingest; its ML-based cues detect touchdowns, interceptions, and other moments, triggering immediate clip creation for social and OTT.
Can I keep my existing encoder and ABR ladder when adding SimaBit?
Yes. SimaBit installs in front of any encoder (H.264, HEVC, AV1, or custom) and outputs a standard RTMP feed to MediaLive. Downstream systems see a normal stream, but at materially lower bitrates for the same perceived quality.
Sources
SimaLabs
©2025 Sima Labs. All rights reserved
SimaLabs
©2025 Sima Labs. All rights reserved
SimaLabs
©2025 Sima Labs. All rights reserved