Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360{\deg} video frames to VR users. Specifically, deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guarantee adequate transmission reliability and resilience under various channel states. Moreover, implementation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR.
翻译:预计无线虚拟现实(VR)将是下一代通信网络的致命应用之一,然而,在有限的带宽资源下,巨大的数据量和对延缓性和可靠性的严格要求,使得非交错无线VR的交付越来越具有挑战性。因此,这些瓶颈促使这项工作寻求使用语义通信的潜力,这是一种新模式,有望大大减轻资源压力,高效交付VR。为此,我们提议了一个新颖的框架,即VR无线Semantic deliveRy(WiserVR),用于向VR用户提供连续360=deg}视频框架。具体地说,基于深层次学习的多个模块对WiserVR的传输器进行了完善设计,以实现高性能特征提取和语义恢复。其中,我们专门开发了语义定位定位图概念,并利用联合语系-语系的调调方法,不仅大大降低通信的延缓度,而且保证在各频道国家下有足够的传输可靠性和复原力。此外,我们还介绍了WiserVR的多种深层次模块,并随后进行了相应的初步测试。