DeepReal: Short-form Video Streaming with Fine-grained Bitrate Adaptation

Short-form video streaming has seen explosive growth in recent years. Typically, the short video apps (e.g., TikTok) are installed on mobile devices, with the video content delivered over wireless networks. Our measurement study shows that almost all the state-of-the-art adaptive bitrate (ABR) algorithms for short video streaming fall short of providing satisfactory Quality-of-Experience (QoE). This is due to their reliance on bandwidth-sensitive adaptation logic with over-discretized encoding bitrate levels, which hampers their ability to manage significant wireless bandwidth fluctuations. To tackle this problem, we propose DeepReal, a fine-grained ABR approach for short videos, the core of which is a novel clustering-augmented continuous control learning algorithm. On the one hand, the clustering offers more expertise for the ABR logic training. On the other hand, the continuous-valued action domain enables the trained ABR logic to support any number of discrete bitrate levels. As a result, DeepReal's bitrate decision is able to effectively adapt to the substantial network dynamics. Large-scale evaluations show that DeepReal improves QoE by 23.5% to 33.3% while maintaining strong spatial and temporal robustness. It offers a tailored and innovative solution for current short-form video services.