Deadline Aware Two-Timescale Resource Allocation for VR Video Streaming

TL;DR

This paper presents a two-timescale resource allocation scheme for VR video streaming that considers frame deadlines and importance, improving quality and fairness under network variability.

Contribution

It introduces a novel two-timescale resource allocation method combining queuing theory and heuristics for VR video streams with deadline and importance considerations.

Findings

Improves VR video quality and fairness under various network conditions.

Effectively balances resource demands with quality guarantees.

Reduces frame dropping and enhances user experience.

Abstract

In this paper, we investigate resource allocation problem in the context of multiple virtual reality (VR) video flows sharing a certain link, considering specific deadline of each video frame and the impact of different frames on video quality. Firstly, we establish a queuing delay bound estimation model, enabling link node to proactively discard frames that will exceed the deadline. Secondly, we model the importance of different frames based on viewport feature of VR video and encoding method. Accordingly, the frames of each flow are sorted. Then we formulate a problem of minimizing long-term quality loss caused by frame dropping subject to per-flow quality guarantee and bandwidth constraints. Since the frequency of frame dropping and network fluctuation are not on the same time scale, we propose a two-timescale resource allocation scheme. On the long timescale, a queuing theory based resource allocation method is proposed to satisfy quality requirement, utilizing frame queuing delay bound to obtain minimum resource demand for each flow. On the short timescale, in order to quickly fine-tune allocation results to cope with the unstable network state, we propose a low-complexity heuristic algorithm, scheduling available resources based on the importance of frames in each flow. Extensive experimental results demonstrate that the proposed scheme can efficiently improve quality and fairness of VR video flows under various network conditions.