Improving QoE-Privacy Tradeoff in XR Streaming

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.