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WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery

Le Xia, Yao Sun, Chengsi Liang, Daquan Feng, Runze Cheng, Yang Yang, Muhammad Ali Imran

TL;DR

This work proposes a novel framework, namely Wireless Semantic delivery for VR (WiserVR), for delivering consecutive 360° video frames to VR users using multiple deep learning-based modules well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery.

Abstract

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° 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.

WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery

TL;DR

This work proposes a novel framework, namely Wireless Semantic delivery for VR (WiserVR), for delivering consecutive 360° video frames to VR users using multiple deep learning-based modules well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery.

Abstract

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° 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.
Paper Structure (12 sections, 5 figures, 1 table)

This paper contains 12 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. How Wireless VR Delivery Enables SemCom?
  3. Process of Typical 360$^{\circ}$ VR Service Provisioning
  4. WiserVR Framework
  5. SLG-based Transceiver Design in WiserVR
  6. WiserVR-Encoder Network
  7. WiserVR-Decoder Network
  8. URLLC Realization with WiserVR
  9. Implementation of WiserVR
  10. Simulation Results and Analysis
  11. Open Research Issues and Outlook
  12. Conclusions

Figures (5)

  • Figure 1: Overview of the proposed WiserVR framework for delivering 360$^{\circ}$ video streaming from the MEC server to VR users.
  • Figure 2: The detailed structure of the WiserVR-Encoder network.
  • Figure 3: The detailed structure of the WiserVR-Decoder network.
  • Figure 4: A schematic diagram of implementing a particular 360$^{\circ}$ VR video delivery based on WiserVR between an MEC server and VR users.
  • Figure 5: PSNR of restored VR video frames versus varying SNR values.