Watching Television Over an IP Network & TV-Watching Behavior Research

1. Watching Television Over an IP Network &TV-Watching Behavior Research presented by Weiping He

2. Already Known: • Television has been a dominant and pervasive mass media since 1950’s • New media (# of channels, video signal) • IPTV

3. Still Unknown or Incompletely known: • Ingrained TV viewing habits • (Monitoring devices at individual homes) • Nielsen Media Research long-standing research effort to estimate TV viewing behaviors through monitoring and surveys.

4. New Weapon to Explore the Unknown: • IPTV: • Enable us to monitor user behavior and network usage of an entire network; • More visibility on TV viewing activities; • Large user base;

5. IPTV Service Architecture • Components: DSLAM, STB, home gateway • IPTV channel switching logs • Record the ICMP messages • Timestamp in units of seconds • IP address of the DSLAM • IP address of the set-top-box (STB) • IP address of the multiple group (channel) • Multiple option of join or leave

7. The studies in this paper • First in-depth analysis of IPTV workloads based on network traces from one of the world’s largest IPTV systems. • 250,000 households, over 6-month period • Characterize the properties of • aggregate viewing sessions • channel popularity dynamics • geographical locality • channel switching behaviors • browsing pattern • user arrival and departure pattern

8. Elements about the experiment: • Channel groups/genre Free, mixed, kids, docu, local, cine, sports, music, news, audio, rest. • Assumption on user modes • Surfing • Viewing • Away Note: Different thresholdscan be used according to particular experiment environment and requirement.

9. Trace Collection • Collection of IPTV channel switching logs from backbone provider. • Record the ICMP messages on the channel changes of 250,000 users. • Process the logs/data • Pre-process the log by excluding non-video multicast groups; • Chronologically sort IGMP join messages; • Analysis the data;

10. Perspectives for Observation • High-level viewing characteristics • Channel popularity and dynamics • Geographical locality • Factors that affect channel changes • Switching from one channel to another • User arrival and departure patterns Section 4 Section 5

11. High-level viewing characteristics • Number of simultaneous online users • Session characteristics • Attention span • Time spent on each genre return

12. Number of simultaneous online users • Friday and Saturday have the lowest evening peaks within the week • On weekends: • # of viewers ramps up • # of distinct viewers +5% • total time spent on TV +30% back

13. Session Characteristics • Average household per day: 2.54 hours and 6.3 distinct channels; • Average length of each online session: 1.2 hours • Median=8s Mean=14.8min The frequency of a TV watching duration increases from 1-4 sec; The graph after 4-sec mark follows a “power-law-distribution” back

14. Attention span • Two steps for channel selections: • browsing content to decide whether to continue or stop streaming • Switching through multiple channel for repeated browsing, until a desired channel is found 50th percentile values range from 6 to 11 seconds; 90th and 95th percentile values range from 3 to 21 minutes. back

15. Time spent on each genre Table 2: Breakdown of popularity across genre (probability of a viewer watching each genre) • There might be some significant difference from those reported by sampling statistics for US population, in term of channel genre population back *Genre categorized “the rest” includes ppv, satellite, and promotional channels.

16. Channel popularity and dynamics (1) • The top 10% of channels account for nearly 80% of viewers; (Pareto principle or 80-20 rule) • This is consistent across different times of the day, regardless of the changing of viewer base over the course of a day. • Calculate the effective number of viewers by the fraction of time a user spent on each channel over a minute period. (Zipf-like distribution)

17. Channel popularity and dynamics (2) • The average viewer shares are similar to that shown in channel popularity; • The graph shows significant fluctuation across the day. • Dissimilarity coefficient ξ = 1 − ρ2, ξ greater than 0.1 is considered to have substantial changes in ranks. return

18. Geographical locality • Locality across regions • Locality across DSLAMs return

19. Locality across regions • The most popular genres are similar across regions: free, mixed, and kids channels are consistently popular; • Users in some regions watch more local channels than those in other regions. back

20. Locality across DSLAMs back return

21. Factors that affect channel changes • Genre clearly affects the likelihood and frequency of channel changes; • Potential factors: the time of day and program popularity; return

22. Switching from one channel to another • Linearvsnonlinear (EPG) • Normalized average probability of channel changes between every pair of channels; • Examine the influence of channel change patterns on viewing.

23. Switching from one channel to another (cont.) • Several interesting channel switching habits in 1st case: • Over 60% of channel changes are linear; • Certain genres show a distinctive pattern of non-linear channel changes within the genres, e.g., free, sports, and kids; • The pattern of linear channel changes continues through the less popular channels like music, satellite, and audio; • The remaining 18% of channel changes are non-linear across different genres. • Distinctive difference between the two cases: • The consecutive viewing of the same channel in the 2nd case accounts for 17% of all viewing instances; • Non-linear viewing patterns in the 2nd case accounts for 67% of viewing instances. • In summary: Viewers tend to continue watching the same channel even after switching for some time and with high probability. return

24. User arrival and departure patterns • Arrival and departure rates • Inter arrival and departure times return

25. Arrival and departure rates • The arrival and departure rates are similar on average. • Several observations: • First, the arrival and departure rates vary over the day. • Second, user departure patterns show consecutive spikes. • Third, the user arrival is much less time-correlated than the departure. back

26. Inter arrival and departure times • Both median CDF of inter-arrival and inter-departure is 0.07 (the same rate); • The arrival rate varies over time and the arrival process is not stationary over the course of a day; backreturn

27. Implications of findings: • Existing and future IPTV systems; • Design of the open Internet TV distribution systems; • Other emerging/potential applications.

28. Any Questions?