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Slide 1 - Advanced Networking Lab

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Slide 1 - Advanced Networking Lab

  1. 1. Watching Television Over an IP Network Meeyoung Cha MPI-SWS Pablo Rodriguez Telefonica Research Jon Crowcroft U. of Cambridge Sue Moon KAIST Xavier Amatriain Telefonica Research ACM IMC 2008
  2. 2. Internet TV (IPTV) <ul><li>Delivering television channels over an IP network </li></ul><ul><li>20 M subscribers worldwide in 2008 </li></ul><ul><li>Popular types </li></ul><ul><ul><li>1. Telco’s nation-wide provisioned service </li></ul></ul><ul><ul><ul><li>By AT&T, France Telecom, Korea Telecom, Telefonica </li></ul></ul></ul><ul><ul><li>2. Web TV </li></ul></ul><ul><ul><ul><li>Joost, Zatoo, VeohTV, Babelgum, BBC’s iPlayer </li></ul></ul></ul><ul><ul><li>3. Box-based video-on-demand </li></ul></ul><ul><ul><ul><li>Apple TV, Vudu box, Sony’s Internet video link </li></ul></ul></ul>
  3. 3. Why study TV viewing patterns? <ul><li>Understanding of human viewing behaviors </li></ul><ul><ul><li>Identify social and demographic aspects, user profiling </li></ul></ul><ul><li>Cost-efficient design of distribution architectures </li></ul><ul><ul><li>Evaluate existing designs and explore new ones </li></ul></ul><ul><li>Design better channel guides and advertisements </li></ul><ul><ul><li>Help people find interesting programs more quickly </li></ul></ul>
  4. 4. Challenges in traditional TV research <ul><li>Nielsen TV rating </li></ul><ul><ul><li>Select representative samples </li></ul></ul><ul><ul><li>Install metering devices at sampled homes </li></ul></ul><ul><ul><li>Extrapolate statistics across a nation </li></ul></ul><ul><li>< Drawbacks > </li></ul><ul><ul><li>Potential bias in sampling </li></ul></ul><ul><ul><li>Awareness to metering may alter user behaviors </li></ul></ul><ul><li>Gathering data from a large number of samples challenging </li></ul><ul><ul><li>IPTV allows for continuous and detailed TV analysis! </li></ul></ul>
  5. 5. A first study on Telco’s IPTV workloads <ul><li>Collected raw data of everybody watching TV </li></ul><ul><ul><li>A quarter million users from a large IPTV system </li></ul></ul><ul><ul><li>(entire subscribers within a nation) </li></ul></ul><ul><ul><li>150 channels including various genres </li></ul></ul><ul><ul><li>(free-to-air, children, sports, movies, music, etc) </li></ul></ul><ul><ul><li>Collected traces for 6 months </li></ul></ul><ul><li>Largest scale study on TV viewing patterns </li></ul><ul><ul><li>User base 10 times larger than the Nielsen’s </li></ul></ul>
  6. 6. Telco’s IPTV service architecture Set-top box TV DSLAM customer premise IP backbone TV head end All 150 channels 1-2 channels
  7. 7. <ul><li>User’s channel change input </li></ul><ul><ul><li>IGMP messages collected across all 700 DSLAMs </li></ul></ul><ul><li>Trace example </li></ul><ul><ul><li>Timestamp </li></ul></ul><ul><ul><li>DSLAM IP </li></ul></ul><ul><ul><li>Set-top box IP </li></ul></ul><ul><ul><li>Multicast channel IP </li></ul></ul><ul><ul><li>Action (join or leave) </li></ul></ul>Data collection Collected here set-top-box DSLAM
  8. 8. Part1. IPTV overview and dataset Part2. Analysis of viewing patterns Part3. Channel change probability
  9. 9. <ul><li>60% channel changes happen within 10 seconds </li></ul><ul><li>Infrastructure must support fast channel changes </li></ul>Channel holding times
  10. 10. Assumptions about user modes <ul><li>Difficulty in inferring user away mode </li></ul><ul><ul><li>TV is OFF; or left ON without any viewer </li></ul></ul><ul><li>Determined active users as those who change channels within a one hour threshold period </li></ul><ul><ul><li>Tested with longer thresholds </li></ul></ul><ul><li>Demarcate viewing from surfing by the minute </li></ul><ul><ul><li>Nielsen also uses 1 minute threshold </li></ul></ul>
  11. 11. <ul><li>Each user in one of the three states at any given time </li></ul><ul><li>Active session: consecutive time spent on surfing or viewing </li></ul>Three user modes
  12. 12. <ul><li>Durations </li></ul><ul><ul><li>An average household watched 2.54 hours of TV and 6.3 channels (distinct) a day </li></ul></ul><ul><ul><li>Each active session lasted 1.2 hours </li></ul></ul><ul><ul><li>Each viewing event lasted 14.8 minutes </li></ul></ul><ul><li>Per content genre </li></ul><ul><ul><li>Average surfing time longer for documentaries and movies (9-11 sec) than news, music, and sports (6-7 sec) </li></ul></ul>Session characteristics
  13. 13. <ul><li>Viewing hours across users highly correlated </li></ul><ul><li>Two peaks at lunch (3PM) and dinner (10PM) times </li></ul>Diurnal pattern
  14. 14. <ul><li>Applied 2-hour thresholds for certain genres (movies, documentaries, sports, etc) </li></ul>Diurnal pattern with longer away threshold
  15. 15. <ul><li>90% of concurrent viewers watch 20% of channels </li></ul><ul><li>Follow the Pareto principal </li></ul>Channel popularity
  16. 16. <ul><li>Viewer share of top channels higher at peak times </li></ul><ul><li>Popularity of top channels reinforced at peak times </li></ul>Time evolution of channel popularity
  17. 17. Implications of viewing patterns <ul><li>60% of channel changes within 10 seconds ( surfing ) </li></ul><ul><li>=> Challenges for P2P-based IPTV systems </li></ul><ul><li>User focus followed the Pareto principal </li></ul><ul><li>=> IP multicast not efficient for unpopular channels </li></ul>
  18. 18. Part1. IPTV overview and dataset Part2. Analysis of viewing patterns Part3. Channel change probability
  19. 19. Channel change patterns <ul><li>Our goal is to understand </li></ul><ul><ul><li>How do people browse through channels? Do they use electronic program guide? </li></ul></ul><ul><ul><li>Do channel changes result in viewing? </li></ul></ul><ul><ul><li>How do users join and leave a particular channel? </li></ul></ul>
  20. 20. Channel change probability <ul><li>Probability of joining channel y after joining channel x </li></ul>60% linear
  21. 21. Channel viewing probability <ul><li>Probability of viewing channel y after viewing channel x </li></ul>67% non-linear 60% within genre 17% to the same channel
  22. 22. User arrival and departure rates <ul><li>Batch-like arrivals and departures </li></ul><ul><li>Inheritance (continued viewing even after channel changes) </li></ul>arrival departure
  23. 23. Implications of channel change patterns <ul><li>Disparity in how we change and view channels </li></ul><ul><li>=> Design of efficient program guide </li></ul><ul><li>High churn, especially during commercial breaks </li></ul><ul><li>=> Challenging for P2P-based IPTV systems </li></ul>
  24. 24. Summary <ul><li>The first work to analyze television viewing patterns from complete raw data of IPTV users </li></ul><ul><li>Implications on the architecture </li></ul><ul><ul><li>Support fast channel changes </li></ul></ul><ul><ul><li>Handle high churn during commercials </li></ul></ul><ul><ul><li>Reflect Pareto channel popularity </li></ul></ul><ul><li>Implications on the viewing guide </li></ul><ul><ul><li>Devise a better way to browse channels </li></ul></ul><ul><ul><li>Personalize suggestions for users </li></ul></ul>
  25. 25. <ul><li>When static 2-hour threshold used for demarcating active and inactive sessions </li></ul>Backup: inferring user modes
  26. 26. Backup: IPTV hot issues <ul><li>How is IPTV different from traditional TV? Why telcos deploy IPTV? </li></ul><ul><li>Modeling TV viewing habits </li></ul><ul><li>Implications on P2P </li></ul>

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