  Proactive tile-based virtual reality (VR) video streaming can use the trace
of FoV and eye movement to predict future requested tiles, then renders and
delivers 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 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 real FoV and define spatial degree of
privacy (SDoP) as a normalized number of camouflaged tile requests. By
consuming more resources to ensure SDoP, the real 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.
