Motion-based rate adaptation in WebRTC videoconferencing using scalable video coding

Abstract

This paper proposes methods for rate adaptation by motion-based spatial and temporal resolution selection in both mesh-connected and selective-forwarding-unit (SFU) connected WebRTC videoconferencing using scalable video coding. In the mesh-connected case, the proposed motion-adaptive spatial/temporal layer selection allows each peer to send video to different peers with different terminal types and network rates at different rates using a single encoder. In the SFU-connected case, motion-adaptive rate control is used both at peers to adapt to the network rate between the sending peer and SFU by spatio-temporal resolution adaptation and at the SFU by layer selection to adapt to the network rate between the SFU and receiving peer. Experimental results show that our proposed motion-based rate adaptation achieves better perceptual video quality with sufficiently high frame rates and lower quantization parameter for video with high motion; and high spatial resolution and lower quantization parameter for video with low motion compared to simple rate-distortion model-based layer selection that does not use motion complexity, at the same rate.