QoEUP: A Preference-Based QoE Optimization Scheme Using Human Feedback for Mobile Video Streaming

With the rising popularity of mobile video streaming, dynamic adaptive streaming over HTTP (DASH)-based bitrate adaptation has garnered significant attention in recent years. Existing studies in this area typically define a unified user quality of experience (QoE) by weighting various metrics, overlooking individual user preferences for QoE. Although some research has considered user preferences, it primarily focuses on optimizing bitrate selection alone, neglecting the joint allocation of communication resources that are tightly coupled with bitrate. In this paper, we propose QoE Optimization Enabler based on User Preference (QoEUP), a scheme for mobile video streaming, which dynamically adjusts bitrate, transmission power, and bandwidth based on channel quality and user preferences during mobility. The proposed scheme begins with training a reference model using deep reinforcement learning without incorporating user preferences. We then develop a user-friendly approach to collect user preferences and create a preference dataset. Finally, leveraging this dataset, we apply advanced direct preference optimization (DPO) to fine-tune the baseline model through supervised learning, effectively integrating individual QoE preferences. Simulation results demonstrate that QoEUP effectively aligns users' actual viewing experiences with their preferences in terms of video quality, playback smoothness, and device energy consumption.