iPlayer and catch-up TV G. Nencioni, N. Sastry, J. Chandaria, J. - - PowerPoint PPT Presentation

Towards a greener and lower network footprint iPlayer and catch-up TV

• G. Nencioni, N. Sastry, J. Chandaria, J. Crowcroft
• Uni. Pisa, King’s College London, BBC R&D, Cambridge

• N. Sastry

About me

 Rich Media + Social Networks

 + Systems support for both

  Data data everywhere…(very keen to share)  Video (meta)data

 Vimeo (AAAI ICWSM 2012)  YouPorn (ACM SIGCOMM IMC 2013)  Gareth yesterday  BBC iPlayer (WWW 2013)  This talk

 Social networks

 Twitter (IEEE/ASE Social Informatics)  London Olympics + London Fashion Week  Pinterest + FB (AAAI ICWSM 2013 + Submission to ACM COSN)

• N. Sastry

Early use of mass media

http://www.watfordobserver.co.uk/nostalgia/memories/10099510.Coronation_treat_as_community_gathers _around_the_only_TV/

Picture from the TV broadcast of the Coronation of Elizabeth II in 1953, Watford

• N. Sastry

Today’s “TV” viewing

With Digital Media Convergence, TV is just another video app, accessed on-demand on the Web

• N. Sastry

What changed: Push Pull

 Superficially: audience to TV set ratio has decreased  At a fundamental level:

 audience per “broadcast” is lower  “Broadcast” time is chosen by the consumer

 Traditional mass media pushed content to consumer  Current dominant model has changed to pull

Generalizes to other mass media as well

• N. Sastry

Implications of the pull model

 Traditionally, “editors” decided what content got pushed when

 Linear TV schedulers use complex analytics to decide “primetime”

 Users get more choice with the pull model

 When to consume  What to consume (from large catalogue)

 Unpopular/niche interest content also gets a distribution channel, not just what editors decide to showcase/bless as “publishable”  Cheaper to stream over the Web to a single user than to broadcast (e.g. to operate/maintain equipment like high power TV transmitters)

 BUT: Cost of broadcast can be amortized across millions of consumers  Could be cheaper per user to broadcast than to stream

• N. Sastry

Understanding and decreasing the network footprint of Catch-up TV

 How does pull model impact delivery infrastructure?  Can additional load of on-demand pulls be reduced by reusing scheduled pushes?  How do users make use of flexibility afforded to them?  Were/are editors good at predicting popularity?

• N. Sastry

Data to answer the questions

 Nearly 6 million users of BBC iPlayer across the UK  32.6 million streams, >37K distinct content items  25% sample of BBC iPlayer access over 2 months

What users prefer to watch-I

• BBC proposes, consumer disposes!
• Serials:~50% of content corpus; 80% of watched content!

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

What users prefer to watch-II

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

What users prefer to watch-III

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Impact of pull on infrastructure

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

On-demand spreads load over time Linear TV schedulers seem to do a good job of predicting popularity!

On-demand more suited to web/pull than linear TV

• BUT: iPlayer traffic is close to 6% of UK peak traffic
• Second only to YouTube in traffic footprint
• Compare to adult video, a traditional heavy hitter. Most popular

adult video streaming sites have <0.2% traffic share

• BUT: amortized per-user, broadcast greener than streaming*

(using Baliga et al.’s energy model for the Internet)

*All channels except BBC Parliament, which has few viewers

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Still, can we decrease its footprint, please?

Yes, we can!

• DVRs have >50% penetration in US, UK
• Many (e.g. YouView) don’t need cable
• Could also use TV tuner and record on laptop

But, people don’t remember to record always

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Can we help users record what they want to watch?

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 Speculative Content Offloading and Recording Engine

SCORE=predictor+optimiser

• Predict using user affinity for
• Episodes of same programme
• Favourite genres
• We can optimise for decreasing traffic or carbon footprint
• Decreasing carbon decreases traffic, but not vice versa
• Turns out we only take 5-15% hit by focusing on carbon

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Performance evaluation

• SCORE saves ~40-60% of savings achieved by oracle
• Green optimisation saves 40% more energy at expense of 5% more traffic

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Compare SCORE relative to Oracle knowing future requests

• Assume finite/limited storage (32GB)
• Sensitivity analysis because calculating

energy per stream is difficult

• We use model by Baliga et al (2009)

Oracle saves:

• Up to 97% of traffic
• Up to 74% of energy
• Savings relatively insensitive to

choice of energy model parameters

Not all of these savings come from predicting popular content

• Indiscriminately recording top n shows can lead to

negative energy savings!

• Personalised approach necessary, despite popularity of

“prime time” content

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

• N. Sastry

Summary

 Characterising on-demand content consumption via 6 million users of BBC iPlayer

 On-demand spreads load over time  Users have strong preferences over genre/duration/serials

 If broadcast is efficient, we should find ways to use it!  SCORE: personalised content offloading engine for TV

 Ideal future aware version saves 97% traffic, 74% energy  Our impl gets 40-60% of ideal, with very simple measures

http://www.inf.kcl.ac.uk/staff/nrs