Traffic-Aware Rate Adaptation for Improving Time-Varying QoE Factors in Mobile Video Streaming

Mobile video has become one of the most valuable services in next-generation heterogeneous networks, and users' quality of experience (QoE) is recognized as its important performance metric. In this paper, we propose an adaptive bitrate (ABR) algorithm to achieve improvement of the timevarying QoE determinants during a mobile video playback. Since watching videos will bring mobile data charges to the users, the traffic consumed by video downloads should be customized to a user-specified amount. To this end, we first analyze the real-world traces, and find the key factors to formulate a continuous QoE model. Then, we introduce a traffic-aware rate adaptation strategy (TARA). Given that users are aware of their real-time capabilities when watching a mobile video, TARA enables robust ABR process which can satisfy users' requirements with traffic constraints. Finally, a centralized reinforcement learning (RL) approach is proposed for the joint optimization of TARA, which aims to maximize the designed QoE metric, and deliver the expected viewing experience to users. Results of experiments driven by both the simulated, and real-world network traces reveal the efficiency of the proposed TARA strategy, and demonstrate its signicant performance improvement as compared to the state-of-the-art ABR algorithms.