KBL: Kettle-style Buffer Loading Algorithm for Short Videos

Swiping the screen to switch videos is a unique browsing behavior for short videos, intended to facilitate viewers in quickly searching for content of interest. However, frequent video switching can result in nearly half of the data being used to transmit never-watched video data, leading to unnecessary network load via resource wastage. To tackle this problem, recent studies have utilized historical viewing data to predict the necessary length of videos to download based on viewing probability. Critically, the precision of the predictions plays a pivotal role in shaping both data consumption and user experience. This paper addresses issues that emerge with inaccurate predictions by proposing KBL (Kettle-style Buffer Loading), a novel algorithm to balance waste with a high-quality video experience, without requiring extensive training or prior knowledge. Inspired by tea kettle service, KBL reduces waste by setting the boundary of the respective video buffers, current and pre-loaded videos, based on an evaluation of the network conditions. Through extensive evaluation, KBL is demonstrated to reduce waste by up to 58% of data usage compared to state of the art short video strategies without incurring significant QoE degradation, even in the face of shifting user behavior.