User-Tailored Video Adaptation in Dynamic Environments

Video streaming applications have become immensely popular, leading to increasing user expectations for high-quality services. Extensive work has been conducted in the areas of Quality of Experience (QoE) modeling and adaptive bitrate (ABR) algorithms to meet this demand. While learning-based approaches have demonstrated substantial progress using large-scale datasets, existing QoE models often focus on system-level metrics, such as bitrate and resolution within the playback buffer, neglecting the quality as perceived by the human eye. Simultaneously, many learning-based ABR algorithms exhibit limited robustness in dynamic environments due to their reliance on a one-size-fits-all strategy, which fails to adapt effectively to complex, real-world conditions. In this article, we propose an integrated system that addresses these limitations by combining an accurate QoE model with an environment-robust adaptation algorithm to enhance user satisfaction in diverse and dynamic environments. First, we introduce RetQoE, a novel approach that accurately estimates the user’s actual QoE by focusing on the quality of video content as perceived by the viewer, rather than on conventional system metrics. Then, we design PVA, a meta-reinforcement-learning-based adaptation that rapidly adjusts its policy to varying environments. We systematically integrate RetQoE and PVA, enabling PVA to update its policy with feedback from RetQoE in just a few steps online. We demonstrate the effectiveness of RetQoE+PVA through extensive evaluations in diverse environments, outperforming conventional-learning-based algorithms across various metrics.