A QoE-Aware Short Video Preload Framework Based on Multi-Stage Stochastic Programming

To enhance user Quality of Experience (QoE) and avoid rebuffering caused by bandwidth degradation in short video applications, it typically sets up a buffer to preload videos in the recommended queue when bandwidth is sufficient. However, users exhibit unique "early departure behavior" during short video browsing, which significantly increases the likelihood of bandwidth waste. Existing methods struggle to balance between QoE and waste rate. This paper proposes a preload framework for short video applications, which includes bandwidth prediction, buffer threshold, and preload decisions. The preload problem is modeled as a stochastic multi-stage optimization problem, and the revenue is divided into immediate decision revenue and future state value. Immediate decision revenue is estimated by predicting user behavior. Solving subproblems at each stage produces a series of fitted hyperplanes for the state value function. The expected state value is then calculated by solving these affine functions, which ultimately determines the optimal loading sequence and bitrate. Experimental results show that, compared to several common algorithms, the proposed framework significantly reduces bandwidth waste while improving QoE.