Online Learning-Based Rate Selection for Wireless Interactive Panoramic Scene Delivery
Online Learning-Based Rate Selection for Wireless Interactive Panoramic Scene Delivery
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DOI：
10.1109/infocom48880.2022.9796965
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Harsh Gupta;Jiangong Chen;Bin Li;R. Srikant
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文献类型：
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作者：
Harsh Gupta;Jiangong Chen;Bin Li;R. Srikant
交互式全景场景传输不仅比相同分辨率的传统视频流多消耗4 ~ 6倍的带宽，而且需要及时显示所传输的内容，以确保交互流畅。由于用户一次只能看到整个场景的大约20%（称为视口），如果我们能够准确预测用户的运动，就足以提供全景场景的相关部分。通常交付的部分要大于视口，以容忍不准确的预测。直观地看，交付部分越大，预测精度越高，无线传输成功概率越低。目标是选择适当的交付部分以最大化系统吞吐量。我们将该问题表述为一个多臂强盗问题，并使用经典的Kullback-Leibler上置信度界（KL-UCB）算法进行部分选择。我们进一步开发了KL-UCB算法的一种新变体，该算法在对所选部分进行每次决策后有效地利用两级反馈（即预测和传输结果），并显示其渐近最优性，这可能是独立的兴趣。我们通过综合模拟和真实实验评估证明了我们提出的算法优于现有启发式方法的性能。
Interactive panoramic scene delivery not only consumes 4∼6× more bandwidth than traditional video streaming of the same resolution but also requires timely displaying the delivered content to ensure smooth interaction. Since users can only see roughly 20% of the entire scene at a time (called the viewport), it is sufficient to deliver the relevant portion of the panoramic scene if we can accurately predict the user’s motion. It is customary to deliver a portion larger than the viewport to tolerate inaccurate predictions. Intuitively, the larger the delivered portion, the higher the prediction accuracy and lower the wireless transmission success probability. The goal is to select an appropriate delivery portion to maximize system throughput. We formulate this problem as a multi-armed bandit problem and use the classical Kullback-Leibler Upper Confidence Bound (KL-UCB) algorithm for the portion selection. We further develop a novel variant of the KL-UCB algorithm that effectively leverages two-level feedback (i.e., both prediction and transmission outcomes) after each decision on the selected portion and show its asymptotical optimality, which may be of independent interest by itself. We demonstrate the superior performance of our proposed algorithms over existing heuristic methods using both synthetic simulations and real experimental evaluations.