Physiological Signal-Driven QoE Optimization for Wireless Virtual Reality Transmission

TL;DR

This paper introduces a novel physiological signal-driven QoE optimization framework for VR streaming, leveraging EEG, ECG, and skin activity to enhance user experience through adaptive transmission strategies.

Contribution

It is the first to integrate physiological signals into QoE modeling and optimize VR transmission using deep reinforcement learning for improved user experience.

Findings

Achieved 88.7% improvement in resolution quality.

Reduced handover events by 81.0%.

Demonstrated effectiveness of physiological signals in QoE optimization.

Abstract

Abrupt resolution changes in virtual reality (VR) streaming can significantly impair the quality-of-experience (QoE) of users, particularly during transitions from high to low resolutions. Existing QoE models and transmission schemes inadequately address the perceptual impact of these shifts. To bridge this gap, this article proposes, for the first time, an innovative physiological signal-driven QoE modeling and optimization framework that fully leverages users' electroencephalogram (EEG), electrocardiogram (ECG), and skin activity signals. This framework precisely captures the temporal dynamics of physiological responses and resolution changes in VR streaming, enabling accurate quantification of resolution upgrades' benefits and downgrades' impacts. Integrated the proposed QoE framework into the radio access network (RAN) via a deep reinforcement learning (DRL) framework, adaptive transmission strategies have been implemented to allocate radio resources dynamically, which mitigates short-term channel fluctuations and adjusts frame resolution in response to channel variations caused by user mobility. By prioritizing long-term resolution while minimizing abrupt transitions, the proposed solution achieves an 88.7\% improvement in resolution and an 81.0\% reduction in handover over the baseline. Experimental results demonstrate the effectiveness of this physiological signal-driven strategy, underscoring the promise of edge AI in immersive media services.