Improving Fairness for QoE of Adaptive Video Streaming over ICN
Rei Nakagawa, Satoshi Ohzahata, Ryo Yamamoto · 2022
Information centric network (ICN) improves the communication quality by flexible content-based control according to application-level information of video content. Then, adaptive video streaming is applied to ICN to improve the client’s quality of experience (QoE). In previous research for adaptive bitrate (ABR) algorithms for ICN, the buffer-based ABR algorithm (BBA), which is designed to change a bitrate according to the video buffer length of video player in a client, is effective for QoE of the single client. However, in terms of QoE fairness among BBA clients sharing a bottleneck, since each client adjusts a bitrate independently, the clients, which communicate with the lower bitrate video, have to decrease their bitrate even when the bottleneck link is occupied by the other clients communicating with the higher bitrate. This results in QoE unfairness and degradation of overall QoE for all clients. To improve overall QoE, this paper presents the approach to decrease bitrate differences among clients by increasing the bitrate of the lower bitrate clients before the bandwidth of bottleneck link is excessively occupied by communications for the higher bitrate clients in terms of QoE fairness. Then, we propose notification-based bitrate control in which distributed servers/routers detect congestion early and explicitly notify the lower bitrate clients to increase the bitrate before the bottleneck link is occupied, while encouraging the highest bitrate clients to decrease for congestion avoidance. Through the simulation experiments, the proposed system improves both QoE fairness and overall QoE metrics.