Dual-Agent Based Collaborative Audio-Video Adaptive Bitrate Strategy under Commuting

Adaptive Bitrate (ABR) is an effective way to improve the user's viewing experience. In recent years, with the increasing demand for high quality and maturing audio-video technology, video-oriented ABR algorithms focusing on various scenarios have been widely studied. However, in commuting scenarios, users' internal demand for audio-video optimization and the external factor of network fluctuations make it difficult for traditional video-oriented ABR algorithms to cope. To address these scenarios' problems, we propose a collaborative audio-video ABR strategy under commuting (COAV). It includes two agents deciding audio and video bitrates respectively to reply to the internal demand and an embeddable prediction module to address the external factor. COAV adopts a centralized training and decentralized execution framework, designs two actors and one critic reinforcement learning neural networks based on Advantage Actor-Critic algorithm, and uses TPA-LSTM to predict the throughput as neural networks state, so as to solve the problem of internal demand and external factor in depth. We evaluate the performance of COAV using trace data collected in commuting scenarios, and the experimental results show that COAV improves the average QoE by about 15.5% compared to RAV baseline.