Privacy-aware VR streaming
Xing Wei, Chenyang Yang

TL;DR
This paper explores how privacy requirements affect proactive VR streaming, proposing a model that balances prediction, computing, and communication to optimize user experience under privacy constraints.
Contribution
It introduces the concept of degree of privacy (DoP), formulates an optimization framework, and provides a closed-form solution to enhance privacy-aware VR streaming performance.
Findings
Privacy requirements are heterogeneous among users.
Increasing DoP can both degrade and improve QoE depending on resource availability.
Optimal resource allocation depends on the level of privacy constraints.
Abstract
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. The quality of experience (QoE) depends on the overall performance of prediction, computing (i.e., rendering) and communication. All prior works neglect that users may have privacy requirement, i.e., not all the current tracking data are allowed to be uploaded. In this paper, we investigate the privacy-aware VR streaming. We first establish a dataset that collects the privacy requirement of 66 users among 18 panoramic videos. The dataset shows that the privacy requirements of 360 videos are heterogeneous. Only 41\% of the total watched videos have no privacy requirement. Based on these findings, we formulate the privacy requirement as the \textit{degree of privacy}…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Visual Attention and Saliency Detection
