  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 before playback. Very recently, privacy protection
in proactive VR video streaming starts to raise concerns. However, existing
privacy protection may fail even with privacy-preserve federated learning. This
is because when the future requested tiles can be predicted accurately, the
user-behavior-related data can still be recovered from the predicted tiles. 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 \textit{camouflaged} tile requests to
the real tile requests and model the privacy requirement as the \textit{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 impacts of sDoP on the
QoE are two sides of the same coin. On the one side the increase of sDoP
improves the capability of communication and computing hence improves QoE. On
the other side it 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.
