Range-Level Preloading With Scalable Watch-Time Estimation for Billion-User Streaming Systems

Yucheng Zhang

doi:10.1109/ton.2026.3697930

Range-Level Preloading With Scalable Watch-Time Estimation for Billion-User Streaming Systems

2026-06-01

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DOI：10.1109/ton.2026.3697930

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Short-video platforms have grown rapidly by allowing users to browse rich media content through seamless swiping. However, the inherently random nature of swipe behavior creates significant challenges for bandwidth efficiency and playback continuity, often resulting in stalls and unnecessary data transfers. We present OffLoad, a new preloading framework that enhances bandwidth efficiency and playback quality using range-based downloading, which generalizes traditional chunk-based preloading to arbitrary-length segments for finer-grained control. At the core of OffLoad is a two-dimensional watch-time estimation model that jointly captures user preferences and video characteristics. Guided by this estimator, OffLoad introduces a hybrid preloading algorithm that integrates heuristic rules with a learning-based module trained directly on large-scale production data, enabling strong generalization in deployment. Following extensive system-level optimization, OffLoad has been deployed on a commercial short-video platform for more than six months. Our A/B testing results show that OffLoad increases overall user watch-time by 1.1‰, while simultaneously reducing 0.13% rebuffering events and 4.92% of bandwidth consumption.

Keywords:

Short video preloading

watch-time estimation

transportation efficiency

quality of experience

deep reinforcement learning

Range-Level Preloading With Scalable Watch-Time Estimation for Billion-User Streaming Systems

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