Deep Reinforcement Learning-Driven Intelligent Panoramic Video Bitrate Adaptation

摘要：

　　Online panoramic video has recently gained enormous popularity.Tile-based adaptive streaming is a promising method to deliver a panoramic video for the sake of bandwidth saving.However,it's challenging to estimate the user's field of view(FoV)and deliver the optimal bitrate due to the dynamic user behavior and time-varying network.In this paper,we propose a novel approach to delivering panoramic video.Specifically,a long short-term memory(LSTM)model is used to estimate the FoV in the next few seconds.Our quality adaptation policy is based on a deep reinforcement learning(DRL)agent,which is able to intelligently adapt its bitrate selection policy to different environments.We have implemented a prototype of this system,which outperforms other existing panoramic video streaming frameworks by 12%in quality of experience(QoE)after getting converged in a wide range of environment metrics,and achieves the best performance.

关键词： Panoramic video FoV estimation tile-based adaptive streaming deep reinforcement learning

作者: Gongwei Xiao Xu Chen Muhong Wu and Zhi Zhou

作者单位: School of Data and Computer Science,Sun Yat-sen University Guangzhou,China

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 565-569

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）