Motion-based Adaptive Streaming in WebRTC using Spatio-Temporal Scalable VP9 Video Coding

WebRTC has become a popular platform for real-time communications over the best-effort Internet. It employs the Google Congestion Control algorithm to obtain an estimate of the state of the network. The default configuration employs single-layer CBR video encoding given the available network rate with rate control achieved by varying the quantization parameter and video frame rate. Recently, some open-source WebRTC platforms provided support for VP9 encoding with spatial scalable encoding option. The main contribution of this paper is to incorporate motion-based spatial resolution adaptation for adaptive streaming rate control and evaluate the use of single-layer (non-scalable) VP9 encoding vs. motion-based mixed spatio-temporal scalable VP9 encoding in point-to-point RTC between two parties in the presence of network congestion. Our results show that, during intervals of high motion activity, spatial resolution reduction with sufficiently high frame rates and reasonable quantization parameter values yield more pleasing video quality compared to the standard rate control scheme employed in open-source WebRTC implementations, which uses only quantization parameter and frame rate for rate control.