A Demand-Aware Adaptive Streaming Strategy for High-Quality WebRTC Videoconferencing

WebRTC videoconferencing has risen to prominence in recent years with a sharp increase in remote work and communication. Conferences with large numbers of participants and/or high-quality video streams can overload network and CPU resources and degrade performance, limiting the use of the technology. Several partial solutions exist to mitigate these issues, such as simulcast and Scalable Video Coding (SVC), but these solutions have shortcomings and require trade-offs that make them inadequate for some scenarios. We propose Demand-Aware Adaptive Streaming (DAAS), a novel method of increasing network and CPU efficiency that reduces stream quality—and thus resource consumption—at the source when possible. The method entails using WebRTC data channels to track the presented resolution of each video stream and adapting stream quality to match presentation requirements. The video publisher maintains full quality when required and adaptively reduces quality when possible. We show that the proposed method has advantages over existing methods in that it reduces network bandwidth and CPU requirements for all participants—senders and receivers—in a WebRTC videoconference.