Probabilistic User Behavior Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
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Enabling DVD-like features in P2P video-on-demand systems
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Peer assisted VoD for set-top box based IP network
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The stretched exponential distribution of internet media access patterns
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Watching television over an IP network
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Modeling user activities in a large IPTV system
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Inside the bird's nest: measurements of large-scale live VoD from the 2008 olympics
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Characterizing interactive behavior in a large-scale operational IPTV environment
INFOCOM'10 Proceedings of the 29th conference on Information communications
A Measurement Study of a Large-Scale P2P IPTV System
IEEE Transactions on Multimedia
Leveraging video viewing patterns for optimal content placement
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part II
Watching videos from everywhere: a study of the PPTV mobile VoD system
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Program popularity and viewer behaviour in a large TV-on-demand system
Proceedings of the 2012 ACM conference on Internet measurement conference
OpenCache: exploring efficient and transparent content delivery mechanisms for video-on-demand
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3DTI amphitheater: a manageable 3DTI environment with hierarchical stream prioritization
Proceedings of the 5th ACM Multimedia Systems Conference
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We investigate how consumers view content using Video on Demand (VoD) in the context of an IP-based video distribution environment. Users today can use interactive stream control functions such as skip, replay, fast-forward, pause, and rewind to control their viewing. The use of these functions can place additional demands on the distribution infrastructure (servers, network, and set top boxes) and can be challenging to manage with a large subscriber base. A model of user interaction provides insight into the impact of stream control on server and bandwidth requirements, client responsiveness, etc. We capture the activity users in a natural setting, viewing video at home. We first develop a model for the arrival process of requests for content. We then develop two stream control models that accurately capture user interaction. We show that stream control events can be characterized by a finite state machine and a sojourn time model, parametrized for major periods of usage (weekend and weekday). Our semi-Markov (SM) model for the sojourn time in each stream control state uses a novel technique based on a polynomial fit to the logarithm of the Inverse CDF. A second constrained model(CM) uses a stick-breaking approach familiar in machine learning to model the individual state sojourn time distributions. The SM model seeks to preserve the sojourn time distribution for each state while the CM model puts a greater emphasis on preserving the overall session duration distribution. Using traces across a period of 2 years from a large-scale operational IPTV environment, we validate the proposed model and show that we are able to faithfully predict the workload presented to a video server. We also provide a synthetic trace developed from the model enabling researchers to also study other problems of interest. We also use the techniques to model consumer viewing of video content recorded on their personal Digital Video Recorder (DVR).