Understanding user behavior in large-scale video-on-demand systems

By Hongliang Yu, Dongdong Zheng, Ben Y. Zhao, Weimin Zheng. European Conference on Computer Systems, 2006

711 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/185b8ffef0311e4ce40f6cbf99635730f6a6f7c8

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In Computer Networks and Communications

Citations in the corpus

Predicting the popularity of online content (2008, background): “Video characteristics and user access frequencies are studied together when streaming media workload is estimated [11, 7, 13, 24 ].”
Statistics and Social Network of YouTube Videos (2008, background): “This is consistent with some previous observations [8], [10], [11], [12] which also found that video accesses on a media server does not follow Zipf’s law.”
On dominant characteristics of residential broadband internet traffic (2009, background): “Other work has looked at media serve r workloads regarding file popularity and temporal propertie s, such as in terms of live media streams collected from a large CDN [4 9], and file reference characteristics and user behavior of a pro duction video-on-demand system in large-scale use [55].”
Network characteristics of video streaming traffic (2011, methodology): “We use the measurements performed by Yu et al. [25] for the Poisson assumption of the arrival rate 2 .”
Scalable and Private Media Consumption with Popcorn (2016, background): “This is not specific to Netflix: similar request rates (average of 6,000 requests in 90 minutes from within a single city) have been reported for other video on demand (VOD) systems [104].”
Developing a predictive model of quality of experience for internet video (2013, background): “Other video measurement studies: Several measurement studies of Internet video have focused on content popularity, user behavior and viewing patterns [22, 41].”
Monitoring web browsing behavior with differential privacy (2014, methodology): “This choice is motivated by the fact that the Poisson process has been commonly used in modeling user page request rate for web browsing [20, 8] and for video-on-demand systems [29].”
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system (2007, methodology): “conducted an in-depth analysis of access patterns and user behaviors in a centralized VoD system [39].”
From QoS to QoE: A Tutorial on Video Quality Assessment (2015, background): “That is to say, a few top videos account for most of the viewer accesses [55], [145], [148], which is usually compared with a Zipf-like distribution.”
Understanding the impact of video quality on user engagement (2011, methodology): “User behavior: Yu et al. present a measurement study of a VoD system deployed by China Telecom [24] focusing on modeling user arrival patterns and session lengths.”
Watching television over an IP network (2008, background): “agrees with observation in other video systems [16,33,34].”