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

TL;DR

This paper presents a novel QoE optimization framework for real-time XR video transmission over wireless networks, balancing low latency, energy constraints, and network variability using advanced deep learning and Lyapunov optimization techniques.

Contribution

It introduces a new QoE model for real-time XR video, formulates an energy-aware optimization problem, and proposes an adaptive bitrate algorithm using LSTM-based DQN for improved transmission quality.

Findings

Proposed algorithm outperforms baselines in QoE metrics.

Reduces video quality variations by up to 50%.

Increases frame transmission success rate by up to 48%.

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%.