Watching Television Over an IP Network

1. ACM IMC 2008 Watching Television Over an IP Network Meeyoung Cha MPI-SWS Pablo Rodriguez Telefonica Research Sue Moon KAIST Jon Crowcroft U. of Cambridge Xavier Amatriain Telefonica Research Presented and modified by : Chervet Benjamin

2. Part2. Analysis of viewing patterns Part3. Channel changeprobability Part1. IPTV overviewand dataset

3. Internet TV (IPTV) • Delivering television channels over an IP network • 20M subscribers worldwide in 2008 • Popular types 1. Telco’s nation-wide provisioned service • By AT&T, France Telecom, Korea Telecom, Telefonica 2. Web TV • Joost, Zatoo, VeohTV, Babelgum, BBC’s iPlayer 3. Box-based video-on-demand • Apple TV, Vudu box, Sony’s Internet video link

4. Why study TV viewing patterns? • Understanding of human viewing behaviors • Identify social and demographic aspects, user profiling • Cost-efficient design of distribution architectures • Evaluate existing designs and explore new ones • Design better channel guides and advertisements • Help people find interesting programs more quickly

5. How me measure TV pattern today ? • Nielsen TV rating • Install a Box that register which Channel is watched. • Every time an user watch the TV he must triggers a button. • The data are then transferred and gathered • All the household is monitored. • Select representative samples • Extrapolate statistics across a nation

6. Challenges in traditional TV research • Nielsen TV rating (con’t) < Drawbacks > • Potential bias in sampling • Awareness to metering may alter user behaviors • Only a few users willing to be so are monitored. • Gathering data from a large number of samples challenging • IPTV allows for continuous and detailed TV analysis!

7. A first study on Telco’s IPTV workloads • Collected raw data of everybody watching TV • A quarter million users from a large IPTV system (entire subscribers within a nation) • 150 channels including various genres (free-to-air, children, sports, movies, music, etc) • Collected traces for 6 months • Largest scale study on TV viewing patterns • User base 10 times larger than the Nielsen’s

8. Telco’s Network Inter ISP Optical Fiber Router Backbone Copper Link DSLAM Backbone DSLAM

9. Telco’s IPTV service architecture TV head end customer premise DSLAM TV All 150 channels IP backbone 1-2 channels Set-top box

10. Data collection • User’s channel change input • IGMP messages collected across all 700 DSLAMs • Trace example • Timestamp • DSLAM IP • Set-top box IP • Multicast channel IP • Action (join or leave) Collected here DSLAM set-top-box

11. Part2. Analysis of viewing patterns Part3. Channel changeprobability Part1. IPTV overviewand dataset

12. Channel holding times • 60% channel changes happen within 10 seconds • Infrastructure must support fast channel changes rough. 20 % of the channels changes occurs between 1 min and 1 hour of watching the same channel

13. Zipf distribution Cumulative mass function Probability mass function The longer an user watch a channel, the less likely he would be to change the channel in the next period.

14. Assumptions about user modes • Difficulty in inferring user away mode • TV is OFF; or left ON without any viewer • Determined active users as those who change channels within a one hour threshold period • Tested with longer thresholds • Demarcate viewing from surfing by the minute • Nielsen also uses 1 minute threshold

15. Three user modes • Each user in one of the three states at any given time • Active session: consecutive time spent on surfing or viewing

16. Session characteristics • Durations • An average household watched 2.54 hours of TV and6.3 channels(distinct) a day • Each active session lasted1.2 hours • Each viewing event lasted 14.8 minutes • Per content genre • Average surfing time longer for documentaries and movies (9-11 sec) than news, music, and sports (6-7 sec)

17. Weekly pattern Party Time Sleep-in

18. Diurnal pattern • Viewing hours across users highly correlated • Two peaks at lunch (3PM) and dinner (10PM) times

19. Diurnal pattern with longer away threshold • Applied 2-hour thresholds for certain genres(movies, documentaries, sports, etc)

20. Inferring user modes • When static 2-hour threshold used for demarcating active and inactive sessions

21. Channel popularity • 90% of concurrent viewers watch 20% of channels • Follow the Pareto principal (80% - 20% rules)

22. Time evolution of channel popularity • Viewer share of top channels higher at peak times • Popularity of top channels reinforced at peak times

23. Implications of viewing patterns • 60% of channel changes within 10 seconds (surfing) => Challenges for P2P-based IPTV systems • User focus followed the Pareto principal => IP multicast not efficient for unpopular channels

24. Part2. Analysis of viewing patterns Part3. Channel changeprobability Part1. IPTV overviewand dataset

25. Channel change patterns • Our goal is to understand • How do people browse through channels? Do they use electronic program guide? • Do channel changes result in viewing? • How do users join and leave a particular channel?

26. Channel change probability • Probability of joining channel y after joining channel x 60% linear

27. Channel viewing probability • Probability of viewing channel y after viewing channel x 67% non-linear 60% within genre 17% to the samechannel

28. User arrival and departure rates • Batch-like arrivals and departures • Inheritance (continued viewing even after channel changes) arrival departure

29. Implications of channel change patterns • Disparity in how we change and view channels => Design of efficient program guide • High churn (attrition rate), especially during commercial breaks => Challenging for P2P-based IPTV systems

30. Summary • The first work to analyze television viewing patterns from complete raw data of IPTV users • Implications on the architecture • Support fast channel changes • Handle high churn during commercials • Reflect Pareto channel popularity • Implications on the viewing guide • Devise a better way to browse channels • Personalize suggestions for users