Understanding and Decreasing the Network Footprint of Catch-up TV G. Nencioni, N. Sastry, J. Chandaria, J. Crowcroft Uni. Pisa, King’s College London, BBC R&D, Cambridge

N. Sastry Early use of mass media _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 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) 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 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