Short video streaming has become a dominant paradigm in digital media, characterized by rapid swiping interactions and diverse media content. A key technical challenge is designing an effective preloading strategy that dynamically selects and prioritizes download tasks from an evolving playlist, balancing Quality of Experience (QoE) and bandwidth efficiency under practical commercial constraints. However, real world analysis reveals critical limitations of existing approaches: (1) insufficient adaptation of download task sizes to dynamic conditions, and (2) watch time prediction models that are difficult to deploy reliably at scale. In this paper, we propose DeLoad, a novel preloading framework that addresses these issues by introducing dynamic task sizing and a practical, multi dimensional watch time estimation method. Additionally, a Deep Reinforcement Learning (DRL) enhanced agent is trained to optimize the download range decisions adaptively. Extensive evaluations conducted on an offline testing platform, leveraging massive real world network data, demonstrate that DeLoad achieves significant improvements in QoE metrics (34.4% to 87.4% gain). Furthermore, after deployment on a large scale commercial short video platform, DeLoad has increased overall user watch time by 0.09% while simultaneously reducing rebuffering events and 3.76% bandwidth consumption.

翻译：短视频流媒体已成为数字媒体的主导范式，其特点是快速滑动交互和多样化的媒体内容。一个关键的技术挑战在于设计有效的预加载策略，该策略需从动态变化的播放列表中动态选择并优先处理下载任务，在实际商业约束下平衡体验质量与带宽效率。然而，现实世界分析揭示了现有方法的关键局限性：(1) 下载任务大小对动态条件的适应性不足；(2) 观看时长预测模型难以可靠地大规模部署。本文提出DeLoad，一种新颖的预加载框架，通过引入动态任务大小调整和一种实用的多维度观看时长估计方法来解决这些问题。此外，训练了一个深度强化学习增强的智能体来自适应地优化下载范围决策。在离线测试平台上利用海量真实网络数据进行广泛评估，结果表明DeLoad在体验质量指标上取得了显著提升（增益达34.4%至87.4%）。此外，在大规模商业短视频平台部署后，DeLoad将整体用户观看时长提高了0.09%，同时减少了卡顿事件并降低了3.76%的带宽消耗。