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$^\circ$ 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.

0+
下载
关闭预览

相关内容

IEEE虚拟现实会议一直是展示虚拟现实(VR)广泛领域研究成果的主要国际场所,包括增强现实(AR),混合现实(MR)和3D用户界面中寻求高质量的原创论文。每篇论文应归类为主要涵盖研究,应用程序或系统,并使用以下准则进行分类:研究论文应描述有助于先进软件,硬件,算法,交互或人为因素发展的结果。应用论文应解释作者如何基于现有思想并将其应用到以新颖的方式解决有趣的问题。每篇论文都应包括对给定应用领域中VR/AR/MR使用成功的评估。 官网地址:http://dblp.uni-trier.de/db/conf/vr/
Top