  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.
