QoE-Aware Dynamic Video Rate Adaptation

Dynamic video streaming protocols allow video clients to adaptively choose video rates to improve viewer Quality-of- Experience (QoE). Existing works on rate adaptation choose the video rate according to network capacity or buffer state, however, they didn´t consider the early quitting problem. Most viewers quit before the video ends, in which case optimal solutions for complete video sessions may become suboptimal. In this paper, we design a video rate adaptation scheme which considers incomplete video sessions and aims at maximizing the quality of the video chunks that are actually watched by viewers. We formulate the video rate adaptation problem as a Markov Decision Process (MDP), in which the viewers´ decision of quitting depends on the video quality and the rebuffer time. To solve the MDP problem, we propose two online learning algorithms for regular viewers and new viewers with no viewing history, respectively. The simulation results show that the proposed scheme increases the video quality {truly} consumed by the viewers, thus improving the viewer QoE.