Predictive Scheduling for Virtual Reality

TL;DR

This paper introduces a predictive scheduling policy for wireless VR systems that leverages user movement predictability to improve video quality and responsiveness, demonstrated through theoretical analysis, prototype implementation, and simulations.

Contribution

It develops a novel joint scheduling policy that accounts for demand prediction uncertainty, enhancing VR experience over wireless networks.

Findings

The policy outperforms default scheduling methods in experiments.

Prototype implementation shows practical feasibility with minor modifications.

Simulation results confirm robustness even with inaccurate predictions.

Abstract

A significant challenge for future virtual reality (VR) applications is to deliver high quality-of-experience, both in terms of video quality and responsiveness, over wireless networks with limited bandwidth. This paper proposes to address this challenge by leveraging the predictability of user movements in the virtual world. We consider a wireless system where an access point (AP) serves multiple VR users. We show that the VR application process consists of two distinctive phases, whereby during the first (proactive scheduling) phase the controller has uncertain predictions of the demand that will arrive at the second (deadline scheduling) phase. We then develop a predictive scheduling policy for the AP that jointly optimizes the scheduling decisions in both phases. In addition to our theoretical study, we demonstrate the usefulness of our policy by building a prototype system. We show that our policy can be implemented under Furion, a Unity-based VR gaming software, with minor modifications. Experimental results clearly show visible difference between our policy and the default one. We also conduct extensive simulation studies, which show that our policy not only outperforms others, but also maintains excellent performance even when the prediction of future user movements is not accurate.