research-article

Watching videos from everywhere: a study of the PPTV mobile VoD system

Authors:

Marc-Ismael Akodjenou,

Mohamed Ali Kaafar,

Gang PengAuthors Info & Claims

IMC '12: Proceedings of the 2012 Internet Measurement Conference

Pages 185 - 198

https://doi.org/10.1145/2398776.2398797

Published: 14 November 2012 Publication History

Abstract

In this paper, we examine mobile users' behavior and their corresponding video viewing patterns from logs extracted from the servers of a large scale VoD system. We focus on the analysis of the main discrepancies that might exist when users access the VoD system catalog from WiFi or 3G connections. We also study factors that might impact mobile users' interests and video popularity. The users' behavior exhibits strong daily and weekly patterns, with mobile users' interests being surprisingly spread across almost all categories and video lengths, independently of the connection type. However, by examining the activity of users individually, we observed a concentration of interests and peculiar access patterns, which allows to classify the users and thus better predict their behavior. We also find a skewed video popularity distribution and then demonstrate that the popularity of a video can be predicted using its very early popularity level. We further analyzed the sources of video viewing and found that even if search engines are the dominant sources for a majority of videos, they represent less than 10% (resp. 20%) of the sources for the highly popular videos in 3G (resp. WiFi) network. We report that both the type of connections and mobile devices in use have an impact on the viewing time and the source of viewing. Based on our findings, we provide insights and recommendations that can be used to design intelligent mobile VoD systems and help improving personalized services on these platforms.

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Cited By

Zhang RHuang TWu CSun L(2023)Who is the Rising Star? Demystifying the Promising Streamers in Crowdsourced Live StreamingIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228881(1-10)Online publication date: 17-May-2023
https://doi.org/10.1109/INFOCOM53939.2023.10228881
Raj RKulkarni ASeetharam ARamesh Ade A. Rocha A(2022)Analyzing Aggregate User Behavior on a Large Multi-platform Content Distribution ServiceAd Hoc Networks and Tools for IT10.1007/978-3-030-98005-4_12(161-172)Online publication date: 27-Mar-2022
https://doi.org/10.1007/978-3-030-98005-4_12
Lee RVenieris SLane N(2021)Deep Neural Network–based Enhancement for Image and Video Streaming Systems: A Survey and Future DirectionsACM Computing Surveys10.1145/346909454:8(1-30)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3469094
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Index Terms

Watching videos from everywhere: a study of the PPTV mobile VoD system
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Published In

cover image ACM Conferences

IMC '12: Proceedings of the 2012 Internet Measurement Conference

November 2012

572 pages

ISBN:9781450317054

DOI:10.1145/2398776

General Chairs:
John Byers
Boston University
,
Jim Kurose
University of Massachusetts Amherst
,
Program Chairs:
Ratul Mahajan
Microsoft Research
,
Alex C. Snoeren
UC San Diego

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGCOMM: ACM Special Interest Group on Data Communication
USENIX Assoc: USENIX Assoc

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2012

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IMC '12

Sponsor:

SIGMETRICS
SIGCOMM
USENIX Assoc

IMC '12: Internet Measurement Conference

November 14 - 16, 2012

Massachusetts, Boston, USA

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

Zhang RHuang TWu CSun L(2023)Who is the Rising Star? Demystifying the Promising Streamers in Crowdsourced Live StreamingIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228881(1-10)Online publication date: 17-May-2023
https://doi.org/10.1109/INFOCOM53939.2023.10228881
Raj RKulkarni ASeetharam ARamesh Ade A. Rocha A(2022)Analyzing Aggregate User Behavior on a Large Multi-platform Content Distribution ServiceAd Hoc Networks and Tools for IT10.1007/978-3-030-98005-4_12(161-172)Online publication date: 27-Mar-2022
https://doi.org/10.1007/978-3-030-98005-4_12
Lee RVenieris SLane N(2021)Deep Neural Network–based Enhancement for Image and Video Streaming Systems: A Survey and Future DirectionsACM Computing Surveys10.1145/346909454:8(1-30)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3469094
Yan HLin TZeng MWu JLi YJin D(2021)Discovering Usage Patterns of Mobile Video Service in the Cellular NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304348218:2(1789-1802)Online publication date: Jun-2021
https://doi.org/10.1109/TNSM.2020.3043482
Yan HFu HLi YLin TWang GZheng HJin DZhao B(2021)On Migratory Behavior in Video ConsumptionIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304346718:2(1775-1788)Online publication date: Jun-2021
https://doi.org/10.1109/TNSM.2020.3043467
Lebreton PYamagishi K(2021)Predicting User Quitting Ratio in Adaptive Bitrate Video StreamingIEEE Transactions on Multimedia10.1109/TMM.2020.304445223(4526-4540)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.3044452
Yan HYang CYu DLi YJin DChiu D(2021)Multi-Site User Behavior Modeling and Its Application in Video RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.292607833:1(180-193)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TKDE.2019.2926078
Li JLi ZTyson GXie G(2020)Characterising Usage Patterns and Privacy Risks of a Home Security Camera ServiceIEEE Transactions on Mobile Computing10.1109/TMC.2020.3039787(1-1)Online publication date: 2020
https://doi.org/10.1109/TMC.2020.3039787
Zhang XYang KZhang RFan QTang HMal Z(2020)Demystifying the Largest Live Game Streaming Platform via Black-Box Measurement2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00056(244-250)Online publication date: Dec-2020
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00056
Baccour EErbad AMohamed ABilal KGuizani M(2019)Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks2019 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2019.8885906(1-7)Online publication date: Apr-2019
https://doi.org/10.1109/WCNC.2019.8885906
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