  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. Very recently,
privacy protection in VR video streaming starts to raise concerns. However,
existing privacy protection may fail even with federated learning at head
mounted display (HMD). This is because when the HMD requests the predicted
requested tiles and the prediction is accurate, the real requested tiles and
corresponding user behavior-related data can still be recovered at multi-access
edge computing server. In this paper, we consider how to protect privacy even
with accurate predictors and investigate the impact of privacy requirement on
the quality of experience (QoE). To this end, we first add extra camouflaged
tile requests in addition to real tile requests and model the privacy
requirement as the spatial degree of privacy (sDoP). By ensuring sDoP, the real
tile requests can be hidden and privacy can be protected. Then, we jointly
optimize the durations for prediction, computing, and transmitting, aimed at
maximizing the privacy-aware QoE given arbitrary predictor and configured
resources. From the obtained optimal closed-form solution, we find that the
increase of sDoP improves the capability of communication and computing hence
improves QoE, but degrades the prediction performance hence degrades the QoE.
The overall impact depends on which factor dominates the QoE. Simulation with
two predictors on a real dataset verifies the analysis and shows that the
overall impact of sDoP is to improve the QoE.
