Buffer-Based Reinforcement Learning For Adaptive Streaming
2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017)(2017)
摘要
Adaptive streaming improves user-perceived quality by altering the streaming bitrate depending on network conditions, trading reduced video bitrates for reduced stall times. Existing adaptation approaches, e.g., rate-based, buffer based, either rely heavily on accurate bandwidth prediction or can be overly-conservative about video bitrates.In this work, we propose a reinforcement learning approach to choose the segment quality during playback. This approach uses only the buffer state information and optimizes for a measure of user-perceived streaming quality. Simulation results show that our proposed approach achieves better QoE than rate-, buffer-based approaches, as well as other reinforcement learning approaches.
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关键词
buffer-based reinforcement learning,adaptive streaming,user-perceived quality,streaming bitrate,network conditions,video bitrates,bandwidth prediction,buffer state information,user-perceived streaming quality,QoE
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