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：无线虚拟现实（VR）有望成为下一代通信网络中的杀手级应用之一。然而，在有限带宽资源下，巨大的数据量以及对延迟和可靠性的严苛要求，使得无绳无线VR传输日益具有挑战性。这些瓶颈因此促使本研究探索利用语义通信（一种有望显著缓解资源压力的新范式）实现高效VR传输的潜力。为此，我们提出了一种名为WiserVR（无线VR语义传输）的新框架，用于将连续的360度视频帧传输给VR用户。具体而言，该框架为WiserVR收发器精心设计了基于深度学习的多模块，以实现高性能特征提取与语义恢复。其中，我们独创性地提出了语义位置图概念，并采用联合语义-信道编码方法与知识共享技术，不仅大幅降低了通信延迟，还能在各种信道状态下保证足够的传输可靠性和鲁棒性。此外，本文还介绍了WiserVR的实现方案，并给出了基于初仿真的性能评估结果，与基准方案进行对比。最后，我们讨论了若干开放性问题并提供了可行解决方案，以充分释放WiserVR的潜力。