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.

翻译：本文研究了多条虚拟现实（VR）视频流共享同一链路时的资源分配问题，重点考虑了每个视频帧的特定截止时间以及不同帧对视频质量的影响。首先，我们建立了一个排队时延界估计模型，使链路节点能够主动丢弃即将超时的帧。其次，基于VR视频的视口特征和编码方法，对不同帧的重要性进行了建模，并据此对各视频流的帧进行排序。随后，我们在满足每流质量保证和带宽约束的条件下，构建了一个最小化因帧丢弃导致的长期质量损失问题。由于帧丢弃频率与网络波动不在同一时间尺度上，我们提出了一种双时间尺度资源分配方案。在长时间尺度上，提出一种基于排队论的资源分配方法，利用帧排队时延界获取每流最小资源需求，以满足质量要求；在短时间尺度上，为快速微调分配结果以应对不稳定的网络状态，我们提出一种低复杂度启发式算法，根据每流中帧的重要性调度可用资源。大量实验结果表明，该方案能在多种网络条件下有效提升VR视频流的质量与公平性。