Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles to be requested before playback. All existing works either focus on designing predictors or allocating computing and communications resources. Yet to avoid the latency, the successively executed prediction, communication, and computing tasks should be accomplished within a predetermined time. Moreover, the quality of experience (QoE) of proactive VR streaming depends on the worst performance of the three tasks. In this paper, we jointly optimize the duration of the observation window for predicting tiles and the durations for computing and transmitting the predicted tiles, aimed at balancing the performance for three tasks to maximize the QoE given arbitrary predictor and configured resources. We obtain the closed-form optimal solution by decomposing the formulated problem equivalently into two subproblems. With the optimized durations, we find a resource-limited region where the QoE increases rapidly with configured resources, and a prediction-limited region where the QoE can be improved more efficiently with a better predictor. Simulation results using three existing predictors and a real dataset validate the analysis and demonstrate the gain from the joint optimization over non-optimized counterparts.

翻译：主动式基于分块的视频流传输可通过在播放前计算并传输预测将被请求的分块，避免无线虚拟现实中的运动到光子延迟。现有工作要么专注于设计预测器，要么专注于分配计算和通信资源。然而为消除延迟，需在预定时间内依次完成预测、通信和计算任务。此外，主动式VR流传输的体验质量取决于这三项任务中最差的表现。本文联合优化用于预测分块的观测窗口时长以及计算和传输预测分块的时长，旨在平衡三项任务的性能，在给定任意预测器和配置资源的情况下最大化QoE。通过将原问题等价分解为两个子问题，我们得到了闭式最优解。利用优化后的时长，我们发现了资源受限区域（其中QoE随配置资源快速增长）和预测受限区域（其中通过改进预测器可更高效提升QoE）。采用三种现有预测器与实际数据集的仿真结果验证了理论分析，并展示了联合优化相比非优化方案的性能增益。