Paper Info
Title
MANTIS: time-shifted prefetching of YouTube videos to reduce peak-time cellular data usage
Abstract
The load on wireless cellular networks is not uniformly distributed through the day, and is significantly higher during peak periods. In this context, we present MANTIS, a time-shifted prefetching solution that prefetches content during off-peak periods of network connectivity. We specifically focus on YouTube given that it represents a significant portion of overall wireless data-usage. We make the following contributions: first, we collect and analyze a real-life dataset of YouTube watch history from 206 users comprised of over 1.8 million videos spanning over a 1-year period and present insights on a typical user's viewing behavior; second, we develop an accurate prediction algorithm using a K-nearest neighbor classifier approach; third, we evaluate the prefetching algorithm on two different datasets and show that MANTIS is able to reduce the traffic during peak periods by 34%; and finally, we develop a proof-of-concept prototype for MANTIS and perform a user study.
Year
DOI
Venue
2020
10.1145/3339825.3391864
MMSys '20: 11th ACM Multimedia Systems Conference Istanbul Turkey June, 2020
Keywords
DocType
ISBN
Edge caching, KNN classification, Prefetching
Conference
978-1-4503-6845-2
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Shruti Lall122.76
Uma Parthavi Moravapalle223.15
Raghupathy Sivakumar32679340.00