期刊
IEEE TRANSACTIONS ON COMMUNICATIONS
卷 68, 期 4, 页码 2491-2508出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2020.2965527
关键词
Streaming media; Delays; Wireless communication; Correlation; Magnetic heads; Solid modeling; Optimization; Mobile virtual reality (VR) streaming; 5G; multicasting; millimeter wave (mmWave); Lyapunov optimization; deep recurrent neural network (DRNN); hierarchical clustering; resource allocation
资金
- Spanish Ministerio de Economia y Competitividad (MINECO) [TEC2016-80090-C2-2-R]
- Basque Government's Department of Education [IT1003-16, IT1294-19]
- Department of Economic Development and Infrastructures
- INFOTECH project NOOR
- Kvantum institute strategic project SAFARI
- Academy of Finland
- 6Genesis Flagship [318927]
Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations of viewers watching 360 degrees HD VR videos are capitalized on to realize a proactive FoV-centric millimeter wave (mmWave) physical-layer multicast transmission. The problem is cast as a frame quality maximization problem subject to tight latency constraints and network stability. The problem is then decoupled into an HD frame request admission and scheduling subproblems and a matching theory game is formulated to solve the scheduling subproblem by associating requests from clusters of users to mmWave small cell base stations (SBSs) for their unicast/multicast transmission. Furthermore, for realistic modeling and simulation purposes, a real VR head-tracking dataset and a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) are leveraged. Extensive simulation results show how the content-reuse for clusters of users with highly overlapping FoVs brought in by multicasting reduces the VR frame delay in 12%. This reduction is further boosted by proactiveness that cuts by half the average delays of both reactive unicast and multicast baselines while preserving HD delivery rates above 98%. Finally, enforcing tight latency bounds shortens the delay-tail as evinced by 13% lower delays in the 99th percentile.
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