A Measurement-Derived Functional Model for the Interaction Between Congestion Control and QoE in Video Conferencing.

Video Conferencing Applications (VCAs) that support remote work and education have increased in use over the last two years, contributing to Internet bandwidth usage. VCA clients transmit video and audio to each other in peer-to-peer mode or through a bridge known as a Selective Forwarding Unit (SFU). Popular VCAs implement congestion control in the application layer over UDP and accomplish rate adjustment through video rate control, ultimately affecting end user Quality of Experience (QoE). Researchers have reported on the throughput and video metric performance of specific VCAs using structured experiments. Yet prior work rarely examines the interaction between congestion control mechanisms and rate adjustment techniques that produces the observed throughput and QoE metrics. Understanding this interaction at a functional level paves the way to explain observed performance, to pinpoint commonalities and key functional differences across VCAs, and to contemplate opportunities for innovation. To that end, we first design and conduct detailed measurements of three VCAs (WebRTC/Jitsi, Zoom, BlueJeans) to develop understanding of their congestion and video rate control mechanisms. We then use the measurement results to derive our functional models for the VCA client and SFU. Our models reveal the complexity of these systems and demonstrate how, despite some uniformity in function deployment, there is significant variability among the VCAs in the implementation of these functions.