Achieving QoE Fairness in Bitrate Allocation of 360° Video Streaming

Abstract

In tile-based 360° video streaming, the users employ the tile rate allocation algorithm to select appropriate bitrate to maximize the quality of experience (QoE). The preferences and viewports, however, can vary significantly across the different users. Since the users independently choose their bitrate according to their own preferences and viewports, it is hard to ensure QoE fairness for users under the constraint of available bandwidth. In this paper, we propose a QoE-fairness aware bitrate allocation algorithm for multi-users (QBAM) to reduce difference of user QoE. According to the trajectory of the user viewpoint and user preferences for video quality, rebuffer time and quality switching, we leverage multi-agent reinforcement learning to train the bitrate allocation strategy. The experimental results show, compared with the current tile rate allocation algorithm, QBAM effectively improves the QoE fairness.