Setting Up WebRTC on AWS Without Losing Your Mind
I spent three weeks last year trying to get bidirectional video streaming working between an embedded Linux device and a browser using the AWS WebRTC SDK in C. The documentation assumes you already know half the stuff that actually trips you up. I am going to walk through what works and where people typically fail. The SDK is essentially a thin wrapper around libwebrtc that handles signaling and media transport to AWS infrastructure. You get TURN-relayed peer connections without managing your own STUN/TURN servers. That part is genuinely useful if you are deploying cameras in environments with aggressive firewalls or CGNAT. The tradeoff is that you are locked into AWS's infrastructure for the control plane, and the C API is unforgiving about memory management. Download the source from the official repository. The SDK lives under the open-source AWS projects structure. Clone it, then run cmake with the proper flags. You need libssl, curl, and the full Chromium-based WebRTC stack as a dependency. Getting libwebrtc built from source takes roughly two hours on a decent machine. Most people skip that step and use the pre-built binaries, which saves time but ties your build to a specific commit hash. Pin your version explicitly or you will get surprised when a silent API change breaks your code later.
The basic flow involves creating a signal client, establishing a peer connection, adding audio and video tracks, exchanging SDP offers and answers through the Kinesis signaling channel, and then handling ICE candidates. I will not paste the full boilerplate because the examples directory covers it adequately. What the examples do not cover is what happens when your embedded device has limited CPU and memory. On a Raspberry Pi 4 with 4GB RAM, running the C sample application for sustained video streaming consumes about 350MB of memory and pushes one core to near 100 percent utilization. That is acceptable for a prototype. It becomes unacceptable when you need five simultaneous streams on the same device. The workaround I used was to drop the video resolution to 480p and the frame rate to 15 fps at the encoder level, not the sink level. Encoding at 720p30 and then downscaling in the browser wastes CPU cycles on both ends. Encode at the lowest quality your use case actually requires. Here is something most guides do not mention. The default bitrate configuration in the SDK examples is too aggressive for most real-world deployments. The SDP negotiation sends a codec preference list that prioritizes VP8 over H.264 in some configurations, and H.264 baseline profile is often a better fit for constrained networks because it has lower decoder complexity. You can set this explicitly in your peer connection configuration before signaling. If you do not, you might spend hours debugging why your ARM-based device chokes on decoding while the browser handles the same stream fine.
Another edge case that cost me a day. When your network switches from WiFi to cellular or experiences high packet loss, the WebRTC connection drops its ICE candidates silently and reconnects. The C SDK fires a state change callback, but if your application does not handle the disconnection gracefully and retry the offer, the stream stays dead until a manual restart. I added a watchdog timer that checks the peer connection state every five seconds and re-triggers the create offer flow if the connection drops to disconnected for more than ten seconds. It is a simple fix that the examples omit because they assume stable network conditions. Signaling through Kinesis Video Streams uses HTTPS WebSocket connections to an AWS endpoint. You need an IoT policy document that grants the device permission to connect to the signaling channel and put media. Setting up the IAM role and policy is straightforward but easy to get wrong. The most common mistake is granting permissions to the wrong resource ARN. Verify your policy with the IAM policy simulator before deploying. I wasted half a day troubleshooting connection refusals that turned out to be a misconfigured resource ARN in the IoT policy. There are real limitations to this approach. Latency typically lands between 200 and 500 milliseconds depending on your region and whether the peers are relayed through TURN. If you need sub-100ms latency, this SDK is the wrong tool. You would be better off running a direct WebRTC deployment with your own servers or using a specialized low-latency protocol. The SDK also does not support selective forwarding or layered codecs out of the box. If you need to stream to hundreds of viewers with adaptive quality, look into Kinesis Video Streams with HTTP Live Streaming instead and use WebRTC only for the bidirectional control path.
Get the Full Details
The C SDK itself is functional but dated. Error handling returns integer error codes that require you to cross-reference header files. There is no structured exception system and logging is minimal. Plan to wrap the SDK in your own error management layer early. Doing it later means refactoring code that already runs in production. For most people building proof of concept live video applications with AWS infrastructure, this SDK gets you from zero to a working bidirectional stream in a weekend. For production deployments, budget extra time for the edge cases around resource constraints, network instability, and IAM configuration. The technology works. The path to getting it to work reliably is not well documented, which is why I am writing this.
