Thompson-Sampling-Based Wireless Transmission for Panoramic Video Streaming
Thompson-Sampling-Based Wireless Transmission for Panoramic Video Streaming
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发表时间：
2020-06
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通讯作者：
Jiangong Chen;Bin Li;R. Srikant
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作者：
Jiangong Chen;Bin Li;R. Srikant
全景视频流因其沉浸式体验而近年来受到广泛关注。与传统视频流不同，在相同分辨率下，它通常消耗 4 美元\约 6 倍的带宽。幸运的是，用户每次只能看到 360° 场景的一部分（大约 20%），因此如果我们能够准确预测用户的运动，就足以提供这部分，即视野（FoV）。在实践中，我们通常提供大于 FoV 的部分以容忍不准确的预测。直观上，交付的部分越大，预测精度越高。然而，这导致传输成功概率较低。目标是选择适当的交付部分以最大化系统吞吐量，这可以表述为多臂老虎机问题，其中每个臂代表交付部分。与具有单一反馈信息的传统老虎机问题不同，在对所选部分做出每次决策之后，我们有两级反馈信息（即预测结果和传输结果）。因此，我们提出了一种基于两级反馈信息的汤普森采样算法，并通过仿真证明了其比传统算法优越的性能。
Panoramic video streaming has received great attention recently due to its immersive experience. Different from traditional video streaming, it typically consumes $4 \approx$ 6× larger bandwidth with the same resolution. Fortunately, users can only see a portion (roughly 20%) of 360° scenes at each time and thus it is sufficient to deliver such a portion, namely Field of View (FoV), if we can accurately predict user’s motion. In practice, we usually deliver a portion larger than FoV to tolerate inaccurate prediction. Intuitively, the larger the delivered portion, the higher the prediction accuracy. This however leads to a lower transmission success probability. The goal is to select an appropriate delivered portion to maximize system throughput, which can be formulated as a multi-armedbandit problem, where each arm represents the delivered portion. Different from traditional bandit problems with single feedback information, we have two-level feedback information (i.e., both prediction and transmission outcomes) after each decision on the selected portion. As such, we propose a Thompson Sampling algorithm based on two-level feedback information, and demonstrate its superior performance than its traditional counterpart via simulations.