Queue-Learning-Based QoE Optimization for Super-Resolution-Assisted Adaptive Video Streaming

High-quality video can provide viewers with a better visual and immersive experience, but it typically requires a significantly higher network bandwidth to accommodate the larger amount of video data. The existing and commonly-used adaptive bitrate (ABR) approaches cannot provide high-quality video services for viewers when the network is poor. To address this issue, we propose an edge super-resolution assisted adaptive video streaming, named SuperABR, to improve viewers' Quality of Experience (QoE) and mitigate the influence of poor networks. First, to prevent the mismatch between the available computing resources and the VSR workload, the dynamics and trends of the available computing capability are extracted from a series of historical VSR reconstructing delays. Second, to optimize the video quality, rebuffering time, and video quality jitter for viewers, we formulate the optimization model by fully considering the states of all caching queues in SuperAbr and imposing probability constraints on the playback queue. Third, a queue-learning-based adaptive video streaming algorithm is proposed in SuperABR to jointly determine the source transmission resolution and the edge VSR-reconstructed resolution, which is essentially a Deep Reinforcement Learning (DRL) method with queue constraints. Finally, extensive experiments illustrate that SuperABR can improve QoE by 20%-76% compared to four baseline algorithms.