TY - GEN
T1 - Dashlet
T2 - 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
AU - Li, Zhuqi
AU - Xie, Yaxiong
AU - Netravali, Ravi
AU - Jamieson, Kyle
N1 - Funding Information:
We thank the anonymous reviewers and our shepherd, Sanjay Rao for their insightful comments. This material is supported in part by NSF CNS grants 2152313, 2153449, 2147909, 2140552, and 2151630, Sloan Research Fellowship, as well as a grant from the Princeton School of Engineering and Applied Science.
Publisher Copyright:
© NSDI 2023.All rights reserved
PY - 2023
Y1 - 2023
N2 - 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 outperforms TikTok by 28-101%, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.
AB - 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 outperforms TikTok by 28-101%, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.
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M3 - Conference contribution
AN - SCOPUS:85159279865
T3 - Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
SP - 1583
EP - 1598
BT - Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
PB - USENIX Association
Y2 - 17 April 2023 through 19 April 2023
ER -