Quality of Experience Optimization for Real-Time XR Video Transmission With Energy Constraints

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Extended Reality (XR) is an important service in the 5G network and in future 6G networks. In contrast to traditional video on demand services, real-time XR video is transmitted frame-by-frame, requiring low latency and being highly sensitive to network fluctuations. In this paper, we model the quality of experience (QoE) for real-time XR video transmission on a frame-by-frame basis. Based on the proposed QoE model, we formulate an optimization problem that maximizes QoE with constraints on wireless resources and long-term energy consumption. We utilize Lyapunov optimization to transform the original problem into a single-frame optimization problem and then allocate wireless subchannels. We propose an adaptive XR video bitrate algorithm that employs a Long Short Term Memory (LSTM) based Deep Q-Network (DQN) algorithm for video bitrate selection. Through numerical results, we show that our proposed algorithm outperforms the baseline algorithms, with the average QoE improvements of 0.04 to 0.46. Specifically, compared to baseline algorithms, the proposed algorithm reduces average video quality variations by 29% to 50% and improves the frame transmission success rate by 5% to 48%.

Original languageEnglish
Pages (from-to)15883-15888
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Volume73
Issue number10
DOIs
Publication statusPublished - 2024
Externally publishedYes

Keywords

  • Adaptive bitrate
  • QoE
  • reinforcement learning
  • wireless extended reality

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