Adaptive <scp>QoE</scp> Optimization via Large Model for Community Health Exercises in Wireless Communication Networks

ABSTRACT This paper proposes AQOF‐LM, an adaptive Quality of Experience (QoE) optimization framework empowered by large‐scale pretrained models and multimodal data fusion through wireless communication platforms to promote population health. AQOF‐LM consists of three key components: a QoE Forecasting Engine Based on Multimodal Semantics, a Context‐Aware Exercise Classifier, and a QoE‐Aware Transmission Controller. The forecasting engine utilizes video, sensor, and biometric signals to predict subjective QoE in real time. The exercise classifier identifies motion context to guide content encoding, while the transmission controller dynamically adjusts delivery strategies under network constraints using a utility‐based policy with hysteresis switching. We implement and evaluate AQOF‐LM on two public multimodal datasets under simulated wireless conditions. Experimental results demonstrate that AQOF‐LM outperforms state‐of‐the‐art baselines in prediction accuracy (MAE = 0.19), delivery stability (QoE stability = 0.92), and latency responsiveness (58 ms). Moreover, AQOF‐LM maintains perceptual quality under bandwidth degradation, achieving up to 25% higher QoE in constrained scenarios. These results establish AQOF‐LM as a scalable and context‐sensitive framework for enhancing real‐time health guidance services in community and low‐resource environments.