  Short video streaming applications have recently gained substantial traction,
but the non-linear video presentation they afford swiping users fundamentally
changes the problem of maximizing user quality of experience in the face of the
vagaries of network throughput and user swipe timing. This paper describes the
design and implementation of Dashlet, a system tailored for high quality of
experience in short video streaming applications. With the insights we glean
from an in-the-wild TikTok performance study and a user study focused on swipe
patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering
mechanism that leverages a simple, non machine learning-based model of users'
swipe statistics to determine the pre-buffering order and bitrate. The net
result is a system that achieves 77-99% of an oracle system's QoE and
outperforms TikTok by 43.9-45.1x, while also reducing by 30% the number of
bytes wasted on downloaded video that is never watched.
