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

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

N. SastryEarly 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. SastryToday’s “TV” viewing

With Digital Media Convergence, TV is just another video app,

accessed on-demand on the Web

N. SastryWhat changed: Push PullSuperficially: audience to TV set ratio has decreasedAt a fundamental level:

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

Traditional mass media pushed content to consumerCurrent dominant model has changed to pull

Generalizes to other mass media as well

N. SastryImplications 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. SastryData 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


What users prefer to watch-III


Abandoned

High preference for 30 and 60 min shows

Impact of pull on infrastructure


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


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


Can we help users record what they want to watch?


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


Performance evaluation

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


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


N. SastrySummary 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

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

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