# Quality of Experience Optimization for Real-time XR Video Transmission with Energy Constraints

arXiv: https://arxiv.org/abs/2405.07689 · HTML: https://mlsanity.com/paper/2405.07689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2024.

## Built on by (most cited first, 1 of 1)

- [Energy Optimization of Multi-task DNN Inference in MEC-assisted XR Devices: A Lyapunov-Guided Reinforcement Learning Approach](https://mlsanity.com/paper/2501.02572/) (2025)

## Abstract (arXiv)

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%.