Real-time Extended Reality Video Transmission Optimization Based on Frame-priority Scheduling

Guangjin Pan*, Shugong Xu*, Shunqing Zhang, Xiaojing Chen, Yanzan Sun

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Extended reality (XR) is one of the most important applications of 5G. For real-time XR video transmission in 5G networks, a low latency and high data rate are required. In this paper, we propose a resource allocation scheme based on frame-priority scheduling to meet these requirements. The optimization problem is modelled as a frame-priority-based radio resource scheduling problem to improve transmission quality. We propose a scheduling framework based on multi-step Deep Q-network (MS-DQN) and design a neural network model based on convolutional neural network (CNN). Simulation results show that the scheduling framework based on frame-priority and MS-DQN can improve transmission quality by 49.9%-80.2%.

Original languageEnglish
Title of host publication2023 International Conference on Future Communications and Networks, FCN 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350396034
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event2023 International Conference on Future Communications and Networks, FCN 2023 - Queenstown, New Zealand
Duration: 17 Dec 202320 Dec 2023

Publication series

Name2023 International Conference on Future Communications and Networks, FCN 2023 - Proceedings

Conference

Conference2023 International Conference on Future Communications and Networks, FCN 2023
Country/TerritoryNew Zealand
CityQueenstown
Period17/12/2320/12/23

Keywords

  • deep Q-network
  • resource allocation
  • wireless extended reality

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