Proactive tile-based virtual reality (VR) video streaming employs the current tracking data of a user to predict future requested tiles, then renders and delivers the predicted tiles to be requested before playback. The quality of experience (QoE) depends on the overall performance of prediction, computing (i.e., rendering) and communication. All prior works neglect that users may have privacy requirement, i.e., not all the current tracking data are allowed to be uploaded. In this paper, we investigate the privacy-aware VR streaming. We first establish a dataset that collects the privacy requirement of 66 users among 18 panoramic videos. The dataset shows that the privacy requirements of 360$^{\circ}$ videos are heterogeneous. Only 41\% of the total watched videos have no privacy requirement. Based on these findings, we formulate the privacy requirement as the \textit{degree of privacy} (DoP), and investigate the impact of DoP on the proactive VR streaming. First, we find that with DoP, the length of the observation window and prediction window of a tile predictor should be variable. Then, we jointly optimize the durations for computing and transmitting the selected tiles as well as the computing and communication capability, aimed at maximizing the QoE given arbitrary predictor and configured resources. From the obtained optimal closed-form solution, we find a resource-saturated region where DoP has no impact on the QoE and a resource-unsaturated region where the two-fold impacts of DoP are contradictory. On the one hand, the increase of DoP will degrade the prediction performance and thus degrade the QoE. On the other hand, the increase of DoP will improve the capability of computing and communication and thus improve the QoE. Simulation results using two predictors and a real dataset validate the analysis and demonstrate the overall impact of DoP on the QoE.

中文翻译：

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隐私感知的VR流

基于主动图块的虚拟现实（VR）视频流使用用户的当前跟踪数据来预测将来请求的图块，然后在回放之前渲染并传递要请求的预测图块。体验质量（QoE）取决于预测，计算（即渲染）和交流的整体性能。所有先前的工作都忽略了用户可能有隐私要求，即，并非所有当前的跟踪数据都允许上传。在本文中，我们研究了具有隐私意识的VR流。我们首先建立一个数据集，收集18个全景视频中66个用户的隐私要求。数据集显示360 $ ^ {\ circ} $视频的隐私要求是异类的。观看的视频总数中只有41％没有隐私保护要求。根据这些发现，我们将隐私权要求公式化为\ textit {privacy degree}（DoP），并研究DoP对主动VR流的影响。首先，我们发现使用DoP时，图块预测变量的观察窗口和预测窗口的长度应该是可变的。然后，我们共同优化计算和传输所选图块的持续时间以及计算和通信能力，旨在最大化给定任意预测变量和配置资源的QoE。从获得的最优封闭形式解中，我们发现DoP对QoE没有影响的资源饱和区域和DoP的双重影响是矛盾的资源不饱和区域。一方面，DoP的增加将降低预测性能，从而降低QoE。另一方面，DoP的增加将提高计算和通信的能力，从而改善QoE。使用两个预测变量和真实数据集的仿真结果验证了该分析并证明了DoP对QoE的总体影响。