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 the task of tile prediction or on the tasks of computing and communications, overlooking the facts that these successively executed tasks have to share the same duration to avoid the latency and 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 to maximize the QoE given arbitrary predictor and configured resources. We obtain the global optimal solution with closed-form expression by decomposing the formulated problem equivalently into two subproblems. With the optimized durations, we find a resource-limited region where the QoE can be improved effectively by configuring more resources, and a prediction-limited region where the QoE can be improved with a better predictor. Simulation results using three existing tile predictors with a real dataset demonstrate the gain of the joint optimization over the non-optimized counterparts.

中文翻译：

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VR视频流的预测、通信和计算时长优化

主动基于区块的视频流可以通过在播放之前计算和交付要请求的预测区块来避免无线虚拟现实 (VR) 的运动到光子延迟。所有现有的工作要么专注于瓦片预测任务，要么专注于计算和通信任务，忽略了这些连续执行的任务必须共享相同持续时间以避免主动 VR 流媒体的延迟和体验质量 (QoE) 的事实取决于三个任务的最差表现。在本文中，我们联合优化了用于预测瓦片的观察窗口的持续时间以及计算和传输预测瓦片的持续时间，以最大化给定任意预测器和配置资源的 QoE。我们通过将公式化的问题等价地分解为两个子问题来获得具有封闭形式表达式的全局最优解。通过优化的持续时间，我们找到了可以通过配置更多资源有效提高 QoE 的资源有限区域，以及可以通过更好的预测器提高 QoE 的预测有限区域。使用三个现有瓦片预测器和真实数据集的模拟结果证明了联合优化相对于非优化对应物的增益。