Enabling the Awareness of Video Perceived Quality for Short-Form Video Streaming

In recent years, fueled by the rapid advances in high-speed mobile networks, streaming short-form videos over mobile devices (e.g., TikTok) has become ubiquitous among mobile users. Despite the widespread application, our investigation based on a real video data source revealed that a large proportion of short videos watched by viewers have suboptimal video quality (e.g., with low VMAF scores), which indicates that the Quality-of-Experience (QoE) is in fact far from optimal. This problem is primarily due to the lack of awareness of video quality optimization based on the features of the video content such as the scene complexity. To tackle this problem, this work develops a novel system called Quality Aware Short Video Streaming (QASVS), which adopts machine learning techniques to learn the video content features and then automatically generate quality-driven bitrate decision models to optimize the perceived video quality and QoE. Extensive evaluations show that QASVS is able to improve the video quality by 11.1%∼27.9% while significantly reducing the playback rebuffering compared to the state-of-the-art streaming algorithms. Therefore, QASVS is able to provide an effective way for streaming vendors to deliver high-performance short-video services.