  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.
