Internet TV Today's and Tomorrow's

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© 2007 Telefónica Investigación y Desarrollo, S.A. Unipersonal

Internet TV Today’s and Tomorrow’s Telefonica’s IPTV architecture and workload analysis


Pablo Rodriguez (Telefonica Research) Meeyoung Cha (MPI-SWS)

ROADS’08 Keynote

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Live content Lower video resolution Tens of channels A few genres Simple channel chagnes A shared experience

Television yesterday

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Television today

Live & on-demand High-definition Content diversity Use of channel guides Personalization Mobility

Internet TV has led to many innovations

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Part 1. IPTV basics

Part 2. Architectural alterna6ves

Part 3. TV viewing habits

Part 4. Future challenges

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Internet TV (IPTV)

  Delivering television channels over an IP network

  Three most popular types

1. Telco’s nation-wide provisioned service

  By AT&T, France Telecom, Korea Telecom, Telefonica

2. Over-the-top box-based video-on-demand

  Apple TV, Vudu box, Sony’s Internet video link

3. Over-the-top Web TV

  Joost, Zattoo, VeohTV, Babelgum, BBC’s iPlayer

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Traffic patterns over the years

  Over-the-top videos already generate more traffic than the entire Internet backbone in 2000

  Videos will be account for nearly 50% of all Internet traffic by 2012

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Internet TV (IPTV)

  Delivering television channels over an IP network

  Three most popular types

1. Telco’s nation-wide provisioned service

  By AT&T, France Telecom, Korea Telecom, Telefonica

2. Over-the-top box-based video-on-demand

  Apple TV, Vudu box, Sony’s Internet video link

3. Over-the-top Web TV

  Joost, Zatoo, VeohTV, Babelgum, BBC’s iPlayer

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Telco IPTV

  Major providers worldwide —  France (Free, Orange, Neuf Cegetal), Hong Kong (PCCW) —  Typically bundled as triple-play (with IP phone and Internet access)

  Number of subscribers 2007-2013

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Telco’s IPTV FAQs

  How is IPTV different from cable or traditional TV? —  In traditional TV, all the channels delivered to the TV set —  IPTV streams only the channel user requests massive content —  Customers assigned unique addresses personalized service

  How is IPTV different from Web-based videos? —  Web services based on kilobits of end-to-end path

(with high-contention in the backbone) —  IPTV requires megabits of steady end-to-end bandwidth

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And multicast finally happened…

  Multicast arrived way too soon, in 1988

  To make matters worse, we also built the killer applications 20 years too soon (e.g., vic, vat, rat)

  …. and we never knew how to monetize them…

  Until, Telco’s took it over and implemented IPTV…!

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Set‐top box

TV

DSLAM

customer premise

TV head‐end

IP backbone

All 150 channels

1‐2 channels

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Internet (1 Mb/s)

VoIP

IPTV (5 Mb/s) 1‐2 channels

Last mile (6 Mb/s)

Scenario of watching live TV

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Set‐top box

TV

IP backbone VoD unicast

Scenario of watching VoD

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TV head‐end Regional VoD servers

Access network

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Part 1. IPTV basics




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  Find paths that connect each destination to TV sources under any failures

TV head-end

Regional Network

Video Hub Office (VHO)

customers

Regional Network

Backbone Distribution Network

TV head-end

VHO

VHO

Broadcast TV VoD

Regional Network

Design goal

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© 2007 Telefónica Investigación y Desarrollo, S.A. Unipersonal 15

Architectural comparison

IP multicast

TV head-end

IP router

DSLAM

STB

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P2P

Architectural comparison

IP multicast

TV head-end

IP router

DSLAM

STB

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cheaper core &

asynchronous/VCR

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Potential P2P‐TV solution for telcos

  Implementation —  Home gateways or set-top boxes —  Dedicated bandwidth for IPTV (downstream) —  Symmetric provisioning (upstream) —  Topology-aware peer selection

  Goal —  Reduce backbone traffic and loads on video servers

while enabling asynchronous and VCR viewing

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  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

Collected at IP mul6cast

switching point

Live TV data collection

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  Static IP multicast can waste huge bandwidth

  Potential problem for serving massive content

Sta6c IP mul6cast (# channels X # DSLAMs)

Actual # streams consumed (on average 17% load of sta6c tree)

High poten6al for p2p IPTV # TV

streams at DSLAMs

Reduction in Edge Links Cost: P2P

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VoD server load without P2P (100%)

VoD load with P2P (5%)

P2P traffic served within DSLAM (80%)

Reduction in VCR server load: P2P

  Storing content at peers allow for rewind features   P2P can reduce the load on the video servers by 95%

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Part 1. IPTV basics




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Traditional TV research

  Nielsen media research —  Pick samples (10,000 homes in USA) —  Install metering devices at sampled homes —  Collect viewing statistics daily —  Extrapolate statistics across a nation

  Drawbacks —  Potential bias in sampling —  Awareness of metering alters user behaviours

  Challenges: gathering data at a large enough scale

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  Collected raw data from a large IPTV system

  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 of TV viewing patterns!

Case study on a Telco’s network

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  60% of all channel changes happen within 10 seconds

  Infrastructure must support fast channel changes

  Assist users find interesting programs more quickly

Channel holding times

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

  Active session: consecutive time spent surfing or viewing

Three user modes

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  Gradual audience ramp‐up (asynchronous arrival)

  High churn of viewers

  Strong membership among users

Per‐program behavior

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  90% of online viewers at any given time watch 20% of channels

  Behavior well-described by the Pareto principle

Channel popularity

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Channel change probability

  Probability of switching to channel y after joining channel x

60% linear

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Channel viewing probability

  Probability of viewing channel y after viewing channel x

67% non‐linear

60% within genre

17% to the same channel

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  3-state Markov model

  Cluster users by deviation from the average pattern

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Browse Away

View

0.77 0.19

0.04 0.57

0.35

0.08

0.47

0.32

0.21

Heavy Browser

Focused Viewer

Light User

Average User

40%

25%

15%

20%

Clustering users by channel change patterns

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Clustering users by active times

  Used Non-negative matrix factorization (NMF) to find 3 types of users

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Night Owls 25%

Always-On 50%

Early-birds 25%

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  IPTV trace methodology —  Complete raw data of viewing patterns (no need to sample)

  Implications for architecture —  Must support for fast channel changes —  Must handle high churn during commercials —  Can reflect the Pareto channel popularity

  Implications for viewing guide —  Need to assist channel browsing in the presence of massive content

(10 hours of content uploaded on YouTube every minute!) —  Need smart search and personalized suggestions

(based on user clustering studies)

Summary of viewing habits

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Part 1. IPTV basics




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Advanced features for the future

  Interactivity —  User participation on the fly, real-time feedback to broadcasters —  Education, games, karaoke on-demand, voting (e.g., American idol)

  Time-shifted or asynchronous —  Watch content at convenient times

  Personalization and search —  Program recommendations, target advertisements —  More opportunities for accessing long tail content —  Synch with mobile TV content —  Customization (e.g., local weather and traffic news)

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Challenges

  Network capability —  Support for end-to-end quality-guaranteed path —  Do we have enough bandwidth to carry all asynchronous traffic?

  Real-time processing of massive data —  Strong need to assist users with content search —  Need interdisciplinary research for better understanding users

  Privacy issues —  Protect user identity —  Sensitive viewing data

  Content diversity issues —  Should Telcos keep walled-garden service?

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Executive summary

  IPTV different from Web applications and imposes challenges on backbones —  live streaming, planetary coverage, end-to-end bandwidth guarantee

  Architectural alternatives —  IP multicast —  Peer-to-peer (P2)

  Workload analysis of TV viewing behaviours —  High user churn —  Zipf-like channel popularity —  Linear channel changes and non-linear viewing —  Strong membership per program

  Future challenges —  Interactivity, time-shifted, long-tail content, personalization

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Lessons learned from our IPTV research

Exci6ng research ahead!

Future architecture must handle high user churn and support massive content and 6me‐shiWed service

Users will need assistance in program discovery & search

IPTV is the largest networking problem to date

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Internet TV Today's and Tomorrow's