Viewpoint and position (VP)-adaptive mixed reality (XR) streaming requires uploading the trajectory of user behavior, thereby causing privacy leakage. Preserving privacy leads to the performance loss of VP prediction of users, subsequently degrading the quality of experience (QoE). This work is the first to improve the QoE-privacy tradeoff for XR streaming. We find the key difference of sample importance for privacy attack and VP prediction, based on which we propose a framework to improve the tradeoff. By designing the noisy entropy function and remapping function, it can achieve a better tradeoff than simply adding the noise to the actual trajectory. The performance of the framework is evaluated with the state-of-the-art VP predictors and practical XR streaming platforms. The results show that the loss of QoE can be mitigated by 55%$\sim$100% to achieve the same privacy level as simply adding noise. When the privacy level achieves the maximum, the QoE is degraded by 0$\sim$3%, compared to VP-adaptive streaming without any privacy-preserving.

Virtual Reality (VR) applications offer an immersive user experience at the expense of privacy leakage caused by inevitably streaming various new types of user data. While some privacy-preserving approaches have been proposed for protecting one type of data, how to design and evaluate approaches for multiple types of user data are still open. On the other hand, preserving privacy will degrade the quality of experience of VR applications or say the utility of user data. How to achieve efficient utility-privacy tradeoff with multiple types of data is also open. Both call for a dataset that contains multiple types of user data and personal attributes of users as ground-truth values. In this paper, we collect a 6 degree-of-freedom VR dataset of 3D virtual worlds for the investigation of privacy-preserving approaches and utility-privacy tradeoff.

Proactive tile-based virtual reality (VR) video streaming can use the trace of FoV and eye movement to predict future requested tiles, then render and deliver the predicted tiles before playback. The quality of experience (QoE) depends on the combined effect of tile prediction and consumed resources. Recently, it has been found that with the field of view (FoV) and eye movement data collected for a user, one can infer the identity and preference of the user. Existing works investigate the privacy protection for eye movement, but never address how to protect the privacy in terms of FoV and how the privacy protection affects the QoE. In this paper, we strive to characterize and satisfy the FoV privacy requirement. We consider "trading resources for privacy". We first add camouflaged tile requests around the actual FoV and define spatial degree of privacy (SDoP) as a normalized number of camouflaged tile requests. By consuming more resources to ensure SDoP, the actual FoVs can be hidden. Then, we proceed to analyze the impacts of SDoP on the QoE by jointly optimizing the durations for prediction, computing, and transmission that maximizes the QoE given arbitrary predictor, configured resources, and SDoP. We find that a larger SDoP requires more resources but degrades the performance of tile prediction. Simulation with state-of-the-art predictors on a real dataset verifies the analysis and shows that a user requiring a larger SDoP can be served with better QoE.

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.

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.

Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles in a segment to be requested before playback. All existing works either focus on tile prediction or on tile computing and delivering, overlooking the facts that these three tasks have to share the same duration and the quality of experience (QoE) depends on the worst performance of them. In this paper, we jointly optimize the duration of the observation window for prediction and the durations used for computing and communication to maximize the QoE of watching a VR video. We find the global optimal solution by decomposing the original problem equivalently into subproblems, with which we find prediction-limited or resource-limited region. Simulation results demonstrate the gain of the optimized durations by using two existing prediction methods with a real dataset.

Wireless virtual reality (VR) can provide unconstrained immersive experience, which however is resource-demanding to satisfy the unique user experience, i.e., motion-to-photon latency and degree of overlap (DoO). To improve the quality of experience with constrained resource, in this paper we propose tile-based proactive VR streaming, which selects, transmits and computes the tiles in a segment most likely requested in future before playback. To select the tiles to be delivered under limited communication and computing resources, it is necessary to learn the user behaviour in requesting tiles in an online manner. We formulate a joint communication and computing duration allocation and tile selection problem to maximize the average DoO for a VR video under the communication and computing resource constraints. To reduce computational complexity and implicitly predict the tile request information, we decouple the original problem into two subproblems, which are respectively solved via convex optimization and hierarchial online learning. Simulation results on a real dataset demonstrate evident gain of the proposed method over the first-predict-then-optimize scheme.