Physiological Signal-driven QoE Optimization for Wireless Virtual Reality Transmission

WU Chang; PENG Mingyu; CHEN Yuang; CHEN Yiyuan; GUO Fengqian; QIN Xiaowei; LU Hancheng

doi:10.11999/JEIT260067

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WU Chang, PENG Mingyu, CHEN Yuang, CHEN Yiyuan, GUO Fengqian, QIN Xiaowei, LU Hancheng. Physiological Signal-driven QoE Optimization for Wireless Virtual Reality Transmission[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260067

Citation:

WU Chang, PENG Mingyu, CHEN Yuang, CHEN Yiyuan, GUO Fengqian, QIN Xiaowei, LU Hancheng. Physiological Signal-driven QoE Optimization for Wireless Virtual Reality Transmission[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260067

Citation:

WU Chang, PENG Mingyu, CHEN Yuang, CHEN Yiyuan, GUO Fengqian, QIN Xiaowei, LU Hancheng. Physiological Signal-driven QoE Optimization for Wireless Virtual Reality Transmission[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260067

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Physiological Signal-driven QoE Optimization for Wireless Virtual Reality Transmission

doi: 10.11999/JEIT260067 cstr: 32379.14.JEIT260067

Information Network Laboratory, University of Science and Technology of China, Hefei 230027, China

Funds: The National Natural Science Foundation of China Joint Fund Key Project (U21A20452)

Received Date: 2026-01-20
Accepted Date: 2026-05-29
Rev Recd Date: 2026-05-18

Available Online: 2026-06-08

Abstract

Abstract

Objective Virtual Reality (VR) has become a transformative medium for immersive digital experiences because it can deliver high-resolution 360° video with ultra-low Motion-To-Photon (MTP) latency. However, its dependence on wireless transmission creates major challenges. Uncompressed data rates above 1 Gbit/(s·Hz) and latency thresholds below 20 ms place stringent demands on network infrastructure. In mobile scenarios, channel fluctuation and user mobility often compromise service continuity and cause abrupt resolution changes. Traditional Quality of Service (QoS) metrics, such as bandwidth, jitter, and packet loss, provide useful network-level information but cannot adequately reflect subjective user satisfaction. Existing Quality of Experience (QoE) models and Adaptive BitRate (ABR) algorithms often use symmetric metrics, such as Mean Opinion Score (MOS), and overlook the fact that users perceive quality deterioration and quality improvement differently. Sudden resolution downgrading has a stronger negative effect on immersion than the positive effect caused by resolution upgrading. This perceptual asymmetry is consistent with behavioral psychology but remains insufficiently addressed in current transmission schemes. In addition, the separation between Radio Access Network (RAN) resource provisioning and application-layer bitrate adaptation often causes mismatched optimization, video-quality oscillation, and resource underuse. To address these issues, this study establishes a quantitative link between physiological responses and resolution changes. It further develops a physiological signal-driven QoE framework integrated with Deep Reinforcement Learning (DRL) to support adaptive transmission, maximize immersion, and reduce the adverse effects of resolution fluctuation in resource-constrained wireless networks. Methods A two-stage method is adopted, including physiological signal analysis and joint optimization framework design. A controlled VR experiment is conducted to quantify the perceptual effect of resolution changes. Nineteen healthy subjects participate in a viewing task using an eye-tracking VR headset, a 32-channel wireless ElectroEncephaloGraphy (EEG) system, ElectroCardioGraphy (ECG) recording, and Galvanic Skin Response (GSR) sensors. The subjects view natural-scene videos in which the resolution levels, including 8K, 4K, 1080P, 720P, and 480P, switch randomly every 8 s. The collected EEG signals are preprocessed by independent component analysis and band-pass filtering. Event-Related Potential (ERP) components are analyzed, with emphasis on the N200 component in the temporal and occipital regions, which reflects visual processing and attention allocation. A Linear Discriminant Analysis (LDA) classifier is used to distinguish different response types. The analysis focuses on the asymmetry between resolution upgrading and downgrading, and on sensitivity to the magnitude of resolution jumps. Based on these physiological findings, a QoE model is formulated by adding penalty terms for resolution degradation and large-amplitude resolution switching. These penalties are weighted more strongly than upgrade rewards to represent user aversion to quality drops. The model is then integrated into an edge-computing environment through a dual-timescale DRL framework. The framework separates control into two cooperative agents: the Scheduling and Utility (SU) agent and the Resolution Scaling (RS) agent. The SU agent operates at the millisecond timescale and performs real-time wireless resource allocation. It uses a Gated Recurrent Unit (GRU) to extract temporal features from Channel State Information (CSI) and transmission history. It then dynamically allocates bandwidth to improve frame delivery success and maintain fairness under VR frame-deadline constraints. The RS agent operates at the frame timescale and determines the resolution of subsequent video frames. Its decision-making is guided by the physiological signal-driven reward function, which penalizes actions that may trigger negative physiological responses, such as sharp resolution drops, unless channel deterioration makes them necessary. Proximal Policy Optimization (PPO) is selected for both agents because of its stable learning behavior in continuous and discrete action spaces. Simulations are conducted using a 3GPP-based wireless channel module with user mobility, shadow fading, and path loss to create a dynamic network environment. Results and Discussions The physiological experiment and network simulations validate the proposed framework. In the physiological analysis, a clear N200 response is observed approximately 200 ms after resolution changes. The N200 amplitude is significantly larger during resolution downgrading than during resolution upgrading (p < 0.001), indicating that users are more sensitive to quality deterioration. Large resolution jumps, such as changes from 8K to 1080P, also induce stronger neural responses and more concentrated occipital energy than minor adjustments. The LDA classifier achieves an average Area Under the Curve (AUC) of 74.12% across 19 subjects, confirming that neural responses contain discriminative information about the direction of resolution change. The GSR results support these findings. A dual-branch GSR feature extraction and classification model reaches an average AUC of 78.10% in distinguishing upward and downward switching events. By contrast, ECG signals do not show a stable effect under the current experimental setting and analysis granularity. Therefore, the subsequent QoE model is mainly constructed from EEG and GSR findings. In the network performance evaluation, the proposed physiological signal-driven DRL framework is compared with several baselines, including Proportional-Fair (PF) scheduling, equal resource allocation, and traditional congestion control represented by SCReAM. The training curves show that the dual-agent system converges and learns to coordinate capacity provisioning with resolution decisions. The SU agent smooths short-term channel fluctuation and provides a stable capacity basis, which enables the RS agent to make more reliable resolution decisions. Quantitative results show that the proposed scheme improves the average video resolution by up to 88.7% compared with the equal-resource baseline. More critically, the resolution switching frequency is reduced by up to 81.0%. This reduction is essential because frequent switching, especially downward switching, causes user discomfort, as demonstrated by the physiological analysis. By prioritizing long-term resolution stability and penalizing abrupt drops through the physiological signal-driven reward function, the proposed system reduces the “ping-pong” effect commonly observed in traditional ABR algorithms. Compared with schemes using different penalty weights, the proposed method achieves a better balance. It avoids overly conservative behavior under large penalties, which lowers the average resolution, and unstable visual quality under small penalties, which increases resolution fluctuation. The joint optimization also allocates resources preferentially to users with urgent frame deadlines or higher risks of perceptible quality degradation, while maintaining a frame delivery success rate above 99%. Conclusions This paper addresses the conflict between wireless-channel instability and the human need for visually consistent VR streaming. By adopting a physiological signal-driven approach, the asymmetric effect of resolution changes on user experience is quantified, which challenges the symmetric assumptions used in traditional QoE models. Integrating this physiological evidence into a dual-timescale DRL framework enables the RAN to go beyond throughput-oriented optimization. Wireless resource allocation supports stable application-layer adaptation, while application-layer demands guide resource scheduling. The proposed solution improves immersive experience by increasing average resolution and reducing the physiologically disruptive effects of sudden quality degradation. The reduction in resolution switching frequency by more than 80% shows that the system can shield users from network variability. This study also indicates the value of edge intelligence in making resource-allocation decisions based on human perception rather than network statistics alone. Future work should extend the QoE model by considering multisensory factors, such as MTP latency, cybersickness, spatial distortion, stalling, and audiovisual synchronization. Individual differences in physiological sensitivity should also be addressed through personalized modeling. For real-world deployment, privacy protection is essential. Federated learning and local edge updates may allow biometric data to be processed locally while supporting global policy optimization. This work provides a human-centric basis for immersive networking and shifts the focus from QoS to physiologically validated QoE.
- Wireless virtual reality,
- physiological signals,
- Quality of Experience (QoE),
- Deep Reinforcement Learning (DRL)

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References(24)

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