“Catch-up", or on-demand access of previously broadcast TV content over the public Internet, constitutes a significant fraction of peak time network traffic. This paper analyses consumption patterns of nearly 6 million users of a nationwide deployment of a catch-up TV service, to understand the network support required. We find that catch-up has certain natural scaling properties compared to traditional TV: The on-demand nature spreads load over time, and users have much higher completion rates for content streams than previously reported. Users exhibit strong preferences for serialised content, and for specific genres.

Exploiting this, we design a Speculative Content Offloading and Recording Engine (SCORE) that predictively records a personalised set of shows on user-local storage, and thereby offloads traffic that might result from subsequent catch-up access. Evaluations show that even with a modest storage of 32GB, an oracle with complete knowledge of user consumption can save up to 74% of the energy, and 97% of the peak bandwidth compared to the current IP streaming-based architecture. In the best case, optimising for energy consumption, SCORE can recover more than 60% of the traffic and energy savings achieved by the oracle. Optimising purely for traffic rather than energy can reduce bandwith by an additional 5%.

This document was originally published in the Proceedings of the 22nd International World Wide Web conference (WWW 2013), pp. 965-976, May 13–17, 2013, Rio de Janeiro, Brazil and the paper authors are Gianfranco Nencioni (University of Pisa), Nishanth Sastry (King's College London), Jigna Chandaria (BBC R&D) and Jon Crowcroft (University of Cambridge)