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
http://www.watfordobserver.co.uk/nostalgia/memories/10099510.Coronation_treat_as_community_gathers
_around_the_only_TV/
Early use of mass media
N. Sastry
Picture from the TV broadcast of the Coronation of Elizabeth II in 1953, Watford
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
N. Sastry
 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
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
N. Sastry
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
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
On-demand spreads load over time
Linear TV schedulers seem to do a
good job of predicting popularity!
Impact of pull on
infrastructure
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 Baliga et 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?
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
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?
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
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)
Oracle saves:
• Up to 97% of traffic
• Up to 74% of energy
• Savings relatively insensitive to
choice of energy model parameters
• SCORE saves ~40-60% of savings achieved by oracle
• Green optimisation saves 40% more energy at expense of 5% more traffic
Performance evaluation
Compare SCORE relative to Oracle knowing future requests
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
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
N. Sastry