Beyond QoE: Diversity Adaption in Video Streaming at the Edge
Chunyu Qiao, Jiliang Wang, Yunhao Liu · 2019
Adaptive bitrate (ABR) algorithms have been critical techniques for high quality-of-experience (QoE) Internet video delivery. Prior work designs ABR algorithms by conducting the overall QoE function of fixed parameters. However, the QoE of end users are diverse and video bitrate may be chosen in a misleading way when leaving out the diversity. State-of-the-art ABR algorithms like MPC, Pensieve utilize off-line modeling techniques and result in performance degradation for online QoE diversity adaption. To address this issue, we propose Elephanta, an online flexible ABR algorithm for edge users which incorporates (1) user QoE perception interface and (2) adaption algorithm with flexible parameters. To avoid overheads for updating parameters online, we model video streaming as a renewal system and formulate specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter setting, we emulate Elephanta under 5 thousand throughput traces including FCC broadband, 3G HSDPA data set from the Internet and 4G/LTE data set collected by ourselves. Accordingly, we implement Elephanta in dash.js at client side for user test. Evaluation results show that Elephanta achieves QoE improvement by 21.1% over MPC, in part for its superior adaptability to QoE diversity.