Slide 1 - Advanced Networking Lab

Watching Television Watching Television Over an IP NetworkOver an IP Network

Meeyoung ChaMPI-SWS

Pablo RodriguezTelefonica Research

Jon CrowcroftU. of Cambridge

Sue MoonKAIST

Xavier AmatriainTelefonica Research

ACM IMC 2008

2Max Planck Institute

Internet TV (IPTV)

Delivering television channels over an IP network

20M subscribers worldwide in 2008

Popular types1. 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


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


Challenges in traditional TV research

Nielsen TV rating Select representative samples

Install metering devices at sampled homes

Extrapolate statistics across a nation

< Drawbacks >

Potential bias in sampling

Awareness to metering may alter user behaviors

Gathering data from a large number of samples challenging IPTV allows for continuous and detailed TV analysis!

5Max Planck Institute 5

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


Set-top box

TV

DSLAM customer premise

TV head end

IP backbone

All 150 channels

1-2 channels

Telco’s IPTV service architecture


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)

set-top-box

DSLAM

Data collection

Collected here

Part1. IPTV overview

and dataset

Part2. Analysis of

viewing patterns

Part3. Channel change

probability


60% channel changes happen within 10 seconds

Infrastructure must support fast channel changes

9

Channel holding times


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


Each user in one of the three states at any given time

Active session: consecutive time spent on surfing or viewing

11

Three user modes


Durations An average household watched 2.54 hours of TV and

6.3 channels (distinct) a day

Each active session lasted 1.2 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)

12

Session characteristics


Viewing hours across users highly correlated

Two peaks at lunch (3PM) and dinner (10PM) times

13

Diurnal pattern


Applied 2-hour thresholds for certain genres(movies, documentaries, sports, etc)

14

Diurnal pattern with longer away threshold


90% of concurrent viewers watch 20% of channels

Follow the Pareto principal

15

Channel popularity


Viewer share of top channels higher at peak times

Popularity of top channels reinforced at peak times

16

Time evolution of channel popularity


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

Part1. IPTV overview

and dataset

Part2. Analysis of

viewing patterns

Part3. Channel change

probability


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?


Channel change probability

Probability of joining channel y after joining channel x

60% linear


Channel viewing probability

Probability of viewing channel y after viewing channel x

67% non-linear

60% within genre

17% to the samechannel


arrival

departure

User arrival and departure rates

Batch-like arrivals and departures

Inheritance (continued viewing even after channel changes)


Implications of channel change patterns

Disparity in how we change and view channels=> Design of efficient program guide

High churn, especially during commercial breaks => Challenging for P2P-based IPTV systems


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

24


When static 2-hour threshold used for demarcating active and inactive sessions

25

Backup: inferring user modes


Backup: IPTV hot issues

How is IPTV different from traditional TV? Why telcos deploy IPTV?

Modeling TV viewing habits

Implications on P2P

Entertainment & Humor

Slide 1 - Advanced Networking Lab