Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction

• URL: http://arxiv.org/abs/2506.19890v1
• Date: Tue, 24 Jun 2025 07:32:34 GMT
• Title: Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction
• Authors: Ziru Zhang, Jiadong Yu, Danny H. K. Tsang,
• Abstract summary: This paper proposes an intelligent framework to maximize quality of experience (QoE) in VR games.<n>It integrates adaptive extraction with causal-aware reinforcement learning (RL)<n> Experiments show that our framework significantly reduces interactive latency, enhances QoE, and maintains fairness.
• Score: 6.2819120598047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The optimization of quality of experience (QoE) in multi-user virtual reality (VR) interactions demands a delicate balance between ultra-low latency, high-fidelity motion synchronization, and equitable resource allocation. While adaptive keyframe extraction mitigates transmission overhead, existing approaches often overlook the causal relationships among allocated bandwidth, CPU frequency, and user perception, limiting QoE gains. This paper proposes an intelligent framework to maximize QoE by integrating adaptive keyframe extraction with causal-aware reinforcement learning (RL). First, a novel QoE metric is formulated using the Weber-Fechner Law, combining perceptual sensitivity, attention-driven priorities, and motion reconstruction accuracy. The QoE optimization problem is then modeled as a mixed integer programming (MIP) task, jointly optimizing keyframe ratios, bandwidth, and computational resources under horizon-fairness constraints. We propose Partial State Causal Deep Deterministic Policy Gradient (PS-CDDPG), which integrates the Deep Deterministic Policy Gradient (DDPG) method with causal influence detection. By leveraging causal information regarding how QoE is influenced and determined by various actions, we explore actions guided by weights calculated from causal inference (CI), which in turn improves training efficiency. Experiments conducted with the CMU Motion Capture Database demonstrate that our framework significantly reduces interactive latency, enhances QoE, and maintains fairness, achieving superior performance compared to benchmark methods.

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