At present, multi-party WebRTC videoconferencing between peers with heterogenous network resources and terminals is enabled over the best-effort Internet using a central selective forwarding unit ...(SFU), where each peer sends a scalable encoded video stream to the SFU. This connection model avoids the upload bandwidth bottleneck associated with mesh connections; however, it increases peer delay and overall network load (resource consumption) in addition to requiring investment in servers since all video traffic must go through SFU servers. To this effect, we propose a new multi-party WebRTC service model over future 5G networks, where a video service provider (VSP) collaborates with a network service providers (NSP) to offer an NSP-managed service to stream scalable video layers using software-defined networking (SDN)-assisted Internet protocol (IP) multicasting between peers using NSP infrastructure. In the proposed service model, each peer sends a scalable coded video upstream, which is selectively duplicated and forwarded as layer streams at SDN switches in the network, instead of at a central SFU, in a multi-party WebRTC session managed by multicast trees maintained by the SDN controller. Experimental results show that the proposed SDN-assisted IP multicast service architecture is more efficient than the SFU model in terms of end-to-end service delay and overall network resource consumption, while avoiding peer upload bandwidth bottleneck and distributing traffic more evenly across the network. The proposed architecture enables efficient provisioning of premium managed WebRTC services over bandwidth-reserved SDN slices to provide videoconferencing experience with guaranteed video quality over 5G networks.
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.
