UBR: User-Centric QoE-Based Rate Adaptation for Dynamic Network Conditions

The prevalence of video streaming applications has led to an escalation in users' demands for high-quality services. Numerous endeavors have been undertaken in the realm of quality-of-experience (QoE) models and adaptive bitrate (ABR) algorithms to fulfill this demand. Nevertheless, the existing QoE models exhibit a significant gap with users' actual experience. ABR algorithms are vulnerable in dynamic network environments. We present an integrated system with an accurate QoE model and an environment-robust adaptation algorithm to ensure high user satisfaction in dynamic network conditions. We define a QoE model that accurately estimates the user's QoE by considering the viewing environment and video content. We then design a meta-reinforcement learning-based adaptation algorithm that adapts to dynamic network conditions. We systematically integrate them, allowing it to update its policy with QoE feedback within a few shots.