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 1950s 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 worlds 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 thresholds can 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% backback

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=8sMean=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: a. browsing content to decide whether to continue or stop streaming b. Switching through multiple channel for repeated browsing, until a desired channel is found 50 th percentile values range from 6 to 11 seconds; 90 th and 95 th percentile values range from 3 to 21 minutes. back

15. Time spent on each genre There might be some significant difference from those reported by sampling statistics for US population, in term of channel genre population backback Genrefreemixedkidsdoculocalcine Viewing prob. Num. channels 38.6% 6 21.5% 19 12.5% 7 6.6% 12 4.9% 17 3.9% 6 GenresportsMusicnewsaudioresttotal Viewing prob. Num. channels 3.8% 8 2.3% 11 1.0% 13 0.3% 15 4.6% 36 100% 150 Table 2: Breakdown of popularity across genre (probability of a viewer watching each genre) *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. o 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 ρ 2, 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 Linear vs nonlinear (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 1 st case: 1. Over 60% of channel changes are linear; 2. Certain genres show a distinctive pattern of non-linear channel changes within the genres, e.g., free, sports, and kids; 3. The pattern of linear channel changes continues through the less popular channels like music, satellite, and audio; 4. 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 2 nd case accounts for 17% of all viewing instances; Non-linear viewing patterns in the 2 nd 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; backback returnreturn

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