Optimizing QoE of Multiple Users over DASH: A Meta-learning Approach

Abstract

Dynamic adaptive video streaming over HTTP (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches concentrate on optimizing the overall quality of experience (QoE) for all client sides, neglecting the QoE diversity of different users. In this paper, we formulate the QoE optimization of multi-user preferences as a multi-task deep reinforcement learning problem, in which QoE refers to the metrics of visual quality, fluctuation and rebuffing events. Then, we propose a meta-learning framework for multi-user preferences (MLMP) as a new DASH adaptation approach. Finally, the simulation results show that the proposed approach outperforms state-of-the-art DASH adaptation approaches in satisfying the different users’ QoE preferences regarding the three metrics.