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Understanding and Decreasing the Network F ootprint of Catch-up TV

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

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Understanding and Decreasing the Network F ootprint of Catch-up TV

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

  2. http://www.watfordobserver.co.uk/nostalgia/memories/10099510.Coronation_treat_as_community_gathers_around_the_only_TV/http://www.watfordobserver.co.uk/nostalgia/memories/10099510.Coronation_treat_as_community_gathers_around_the_only_TV/ Early use of mass media Picture from the TV broadcast of the Coronation of Elizabeth II in 1953, Watford

  3. Today’s “TV” viewing With Digital Media Convergence, TV is just another video app, accessed on-demand on the Web

  4. What changed: Push Pull Generalizes to other mass media as well • 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 pushedcontent to consumer • Current dominant model has changed to pull

  5. 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

  6. 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?

  7. 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

  8. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 • BBC proposes, consumer disposes! • Serials:~50% of content corpus; 80% of watched content! What users prefer to watch-I

  9. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 What users prefer to watch-II

  10. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 High preference for 30 and 60 min shows Abandoned What users prefer to watch-III

  11. 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! Impact of pull on infrastructure

  12. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 • 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 Baligaet al.’s energy model for the Internet) *All channels except BBC Parliament, which has few viewers On-demand more suited to web/pull than linear TV Still, can we decrease its footprint, please?

  13. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 • DVRs have >50% penetration in US, UK • Many (e.g. YouView) don’t need cable • Could also use TV tuner and record on laptop Yes, we can! But, people don’t remember to record always

  14. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 Speculative Content Offloading and Recording Engine Can we help users record what they want to watch?

  15. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 • 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 SCORE=predictor+optimiser

  16. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 • Assume finite/limited storage (32GB) • Sensitivity analysis because calculating energy per stream is difficult • We use model by Baliga et al (2009) • SCORE saves ~40-60% of savings achieved by oracle • Green optimisation saves 40% more energy at expense of 5% more traffic • Oracle saves: • Up to 97% of traffic • Up to 74% of energy • Savings relatively insensitive to choice of energy model parameters Performance evaluation Compare SCORE relative to Oracle knowing future requests

  17. Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13 • Indiscriminately recording top n shows can lead to negative energy savings! • Personalised approach necessary, despite popularity of “prime time” content Not all of these savings come from predicting popular content

  18. Summary http://www.inf.kcl.ac.uk/staff/nrs • Characterisingon-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

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