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Duration-Squeezing-Aware Communication and Computing for Proactive VR
arXiv - CS - Multimedia Pub Date : 2021-01-03 , DOI: arxiv-2101.00611
Xing Wei, Chenyang Yang, Shengqian Han

Proactive tile-based virtual reality video streaming computes and delivers the predicted tiles to be requested before playback. All existing works overlook the important fact that computing and communication (CC) tasks for a segment may squeeze the time for the tasks for the next segment, which will cause less and less available time for the latter segments. In this paper, we jointly optimize the durations for CC tasks to maximize the completion rate of CC tasks under the task duration-squeezing-aware constraint. To ensure the latter segments remain enough time for the tasks, the CC tasks for a segment are not allowed to squeeze the time for computing and delivering the subsequent segment. We find the closed-form optimal solution, from which we find a minimum-resource-limited, an unconditional and a conditional resource-tradeoff regions, which are determined by the total time for proactive CC tasks and the playback duration of a segment. Owing to the duration-squeezing-prohibited constraints, the increase of the configured resources may not be always useful for improving the completion rate of CC tasks. Numerical results validate the impact of the duration-squeezing-prohibited constraints and illustrate the three regions.

中文翻译:

主动VR的持续时间压缩感知通信和计算

基于主动图块的虚拟现实视频流计算并交付要播放的预测图块。现有的所有工作都忽略了一个重要的事实,即一个网段的计算和通信(CC)任务可能会压缩下一个网段的任务时间,这将导致后一个网段的可用时间越来越少。在本文中,我们共同优化CC任务的持续时间,以在任务持续时间感知约束下最大化CC任务的完成率。为了确保后面的段留有足够的时间来执行任务,不允许某个段的CC任务浪费时间来计算和交付后续段。我们找到了封闭形式的最优解,从中我们找到了最小资源限制,无条件和有条件的资源权衡区域,由主动CC任务的总时间和片段的播放持续时间确定。由于持续时间压缩的限制,配置资源的增加对于提高CC任务的完成率可能并不总是有用的。数值结果验证了持续时间压缩约束的影响,并说明了这三个区域。
更新日期:2021-01-05
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