How We Set Up Live Streams That Handle 50,000 Viewers
Live streaming at scale is hard. Here's our approach to building broadcasts that don't fall apart when it matters most.
Live streaming is unforgiving. If a website has a hiccup, you refresh the page. If a live stream drops during a keynote or a concert, the moment is gone. That's why the architecture decisions matter more here than in almost any other type of project.
The stack we typically use: video ingest through RTMP, transcoding via AWS MediaLive or a dedicated server depending on the budget, delivery through CloudFront CDN with HLS adaptive bitrate streaming, and a custom player built on hls.js or Video.js.
Adaptive bitrate is the first thing most people skip and the first thing that causes problems. Your viewers are on different connections. Some are on fiber, some are on spotty hotel wifi, some are on cellular. If you push a single quality level, the people on bad connections get buffering and the people on good connections get lower quality than they could handle. ABR solves this automatically.
We test with simulated load before every event. Not just 'does it work with 100 people' but 'what happens at 10,000, 30,000, 50,000 concurrent connections.' We've caught issues at 15,000 that would have been invisible at 5,000. The CDN configuration, the origin server capacity, the WebSocket connections for chat - each of these has its own scaling curve.
Chat and interaction during streams add another layer. WebSocket connections scale differently than video streams. We typically use a managed service for chat (like PubNub or a custom Redis-backed solution) so the chat infrastructure doesn't compete with video delivery.
Latency is the other big consideration. Standard HLS has 15-30 seconds of delay. For events where audience interaction matters (Q&A, polls, live auctions), we bring this down to 3-5 seconds using Low-Latency HLS or WebRTC for small audiences.
Every live event we've done has had at least one surprise. A venue's internet was slower than promised, a camera feed dropped, a CDN region had elevated error rates. The difference between a smooth broadcast and a disaster is having fallback plans for all of it. Redundant ingest, automatic failover, and someone monitoring the whole thing in real time.