AI in Live Streaming and How It’s Changing the Technology

In this blog, based on my recent LinkedIn post, you’ll learn about AI-powered capabilities in live streaming, which live streaming use cases can benefit from them, what challenges could be solved, and how AI integrates with Red5 Pro and Red5 Cloud.

How AI Can Be Utilized in Live Streaming

If you prefer video format, watch this recording on Youtube:

I’ll explain how this works later in the blog, but first, let’s talk about the scenarios and use cases where this can be applied and how it can benefit businesses and organizations.

What Use Cases Can Benefit From Using AI-Powered Capabilities in Live Streaming?

I’ll name a few scenarios and the advantages they get to give you an idea of how AI can be applied in live streaming:

• Live video monitoring and surveillance: Security, facility monitoring, and IP camera streaming. They can benefit from identifying people trespassing property and notify about it, identifying whether workers are wearing hard hats on construction sites or masks in hospitals, tracking animals across farms and pastures, and more. According to Research and Market, the AI in video surveillance market is forecasted to grow by USD 10.89 billion during 2024-2029, accelerating at a CAGR of 22.7% during the forecast period.
• Government Tech: Drone streaming, traffic monitoring, and defense use cases benefits from detecting crowd and vehicle congestion and automatic traffic rerouting, identifying early signs of forest fire and floods, car plates, flagging accidents on highways, and inspecting critical infrastructure.
• Broadcast scenarios: News and sports broadcasting, production workflows. These use case can automatically blur out inappropriate gestures from fans, nudity, any other details important for organization.
• Social media applications: Setup instant blur out triggers for content like nudity or violence, shut down streams when they violate platform rules.

The next part is more technical and explains how this actually works.

How AI Integrates with Live Streaming Platforms?

I’ll explain with an example how we approach this at Red5 by integrating AI with Red5 Pro and Red5 Cloud. You can also read more about this in our ‘IBC 2025 recap‘ and ‘AI Detection Is Set to Transform Live Streaming‘
blog posts.

At the core of our approach is a real-time frame extraction process that allows AI models to analyze video and audio data almost instantly. This system works both within the Red5 Cloud XDN real-time streaming environment and with HTTP-based streaming, such as HLS. By supporting near-instant frame extraction at sub-second intervals, Red5 enables AI-assisted applications that operate in real time without interrupting the live stream.

We extract frames in real-time and send them to an AI model to look at what’s happening in it and give you some feedback on it. Here is what happens step-by-step:

1. Create the Stream: A remote encoder like Osprey Talon (read this study about our joint solution), Videonencoders**,** OBS Studio, or FFmpeg for software-based encoders or broadcaster pushes a stream into Red5.
2. Handle the Ingest: Red5 is a protocol-agnostic platform. We support multiple protocols including WebRTC, RTSP, SRT, RTMP, ERTMP, Zixi, WHIP. A Red5 Origin node receives and manages the incoming live stream.
3. Extract the Image: Using Red5’s Brew API, a custom service extracts frames/images from the live stream with AVC (H.264) decode process. It accepts a full frame of Annex-B formatted NAL units with 0x0001 start codes in one call. Decodes frames into raw packed YUV format one at a time. Returns frame metadata: presence, width, height, format. We then convert to a PNG or JPEG to pass on to the AI model.
4. Run AI Processing:
   – Start by configuring the AI Model. Each frame is scored 0–100 based on event severity. Predefine categories (e.g., congestion, accidents, fire) guide evaluation. Set up prompts that include detailed definitions to ensure consistency. LLM returns structured scores in JSON for each category in real time.
   – Send Data to the AI Model. Extracted frames are pushed to cloud storage, such as Amazon S3, where the model monitors for new frames that appear for analysis and formatting. Implement a shared file system by mounting an NFS drive that both systems have access to. Use a WebSocket between the Red5 instance and the GPU instance running the VLM to push the image data.
   ⁃ Run Everything on the Same Machine. Leverage a powerful GPU + CPU to run both streaming and AI processes on the same bare metal or cloud VM.
5. Encode New Image: AI-processed content is fed back into the Brew API to re-encode in real-time as a live stream.
6. Send Out the Egress Stream: Red5 publishes the modified stream for distribution. Again we have a lot of choices with Red5 (WebRTC, WHEP, SRT, RTMP, RTSP, HLS, LL-HLS, and soon MOQ).

The integration is AI-agnostic, meaning users can apply pre-integrated large language models (LLMs) or visual language models (VLMs) available in Red5 Cloud, or bring their own models using open APIs. This makes it easier to use AI for tasks such as object recognition, speech-to-text transcription, anomaly detection, or content moderation directly within a live stream. Red5 also partners with other innovative AI service providers such as Nomad Media, Magnifi, PubNub, The Famous Group, Oracle Cloud Infrastructure, AWS, and more.

By combining real-time frame extraction with XDN’s low-latency infrastructure, these AI operations can run in parallel with video transport without adding noticeable delay. Whether it’s a 4K WebRTC stream or a high-latency HTTP-based feed, the process stays consistent and efficient.

In both Red5 Pro and Red5 Cloud environments, this process helps maintain sub-250 ms latency while allowing AI-enhanced video streams to be distributed across any supported protocol. The same architecture also supports exporting extracted frames for offline AI use cases such as generating thumbnails, highlighting key sports moments, or detecting production defects in industrial streams.

Conclusion

AI in live streaming is no longer just a buzzword. It is redefining what is possible in real-time video. What excites me most is not just the efficiency. It is the shift in how humans interact with live video. Operators can stop scanning endless feeds and focus on responding to actionable insights surfaced by AI. For developers, tools in Red5 Cloud will soon make integrating these capabilities much easier.

This space is wide open. From video surveillance and traffic monitoring to custom advertising to interactive fan experiences, we are only scratching the surface of what AI can do for live streaming.