Physiological Signal-Driven QoE Optimization for Wireless Virtual Reality Transmission

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. 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% Experimental results demonstrate the effectiveness of this physiological signal-driven strategy, underscoring the promise of edge AI in immersive media services. While this technology enables unprecedented engagement in applications ranging from event viewing to interactive education, its reliance on wireless transmission poses critical challenges. The uncompressed data rates exceeding 1 Gbps and latency thresholds below 20 ms impose stringent demands on network infrastructure, particularly in mobile scenarios where channel fluctuations and user mobility degrade service consistency. Traditional quality of service (QoS) metrics (e.g., bandwidth, jitter, and packet loss) provide necessary but insufficient insights into user satisfaction, necessitating perceptual quality of experience (QoE) frameworks tailored to user's unique requirements [3]-[5].