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）被视作下一代通信网络中的关键应用之一。然而，海量数据量以及有限带宽资源下对低延迟和高可靠性的严苛要求，使得无绳无线VR传输日益具有挑战性。此类瓶颈因此促使本研究探索语义通信的潜力——这一新范式有望显著缓解资源压力，以实现高效的VR传输。为此，我们提出名为“无线VR语义传输”（WiserVR）的新型框架，用于向VR用户传输连续的360°视频帧。具体而言，WiserVR的收发端设计了基于深度学习的多模块，以实现高性能特征提取和语义恢复。其中，我们专门提出了语义位置图的概念，并采用联合语义-信道编码方法与知识共享机制，不仅显著降低通信延迟，还能保证在多种信道状态下具有足够的传输可靠性和鲁棒性。此外，本文给出了WiserVR的实现方案，并通过初步仿真与基准方法进行了性能对比评估。最后，我们讨论了若干开放性问题，并提出了可行解决方案以充分释放WiserVR的潜力。