SCORE: Exploiting Global Broadcasts to Create Offline Personal Channels for On-Demand Access

The last 5 years have seen a dramatic shift in media distribution. For decades, TV and radio were solely provisioned using push-based broadcast technologies, forcing people to adhere to fixed schedules. The introduction of catch-up services, however, has now augmented such delivery with online pull-based alternatives. Typically, these allow users to fetch content for a limited period after initial broadcast, allowing users flexibility in accessing content. Whereas previous work has investigated both of these technologies, this paper explores and contrasts them, focusing on the network consequences of moving towards this multifaceted delivery model. Using traces from nearly 6 million users of BBC iPlayer, one of the largest catch-up TV services, we study this shift from push- to pull-based access. We propose a novel technique for unifying both push- and pull-based delivery: the Speculative Content Offloading and Recording Engine (SCORE). SCORE operates as a set-top box, which interacts with both broadcast push and online pull services. Whenever users wish to access media, it automatically switches between these distribution mechanisms in an attempt to optimize energy efficiency and network resource utilization. SCORE also can predict user viewing patterns, automatically recording certain shows from the broadcast interface. Evaluations using our BBC iPlayer traces show that, based on parameter settings, an oracle with complete knowledge of user consumption can save nearly 77% of the energy, and over 90% of the peak bandwidth, of pure IP streaming. Optimizing for energy consumption, SCORE can recover nearly half of both traffic and energy savings.

[1]  Nishanth R. Sastry,et al.  On factors affecting the usage and adoption of a nation-wide TV streaming service , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[2]  Nishanth R. Sastry,et al.  ISP-friendly peer-assisted on-demand streaming of long duration content in BBC iPlayer , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[3]  Vijay Erramilli,et al.  Is there a case for mobile phone content pre-staging? , 2013, CoNEXT.

[4]  Kyunghan Lee,et al.  Mobile Data Offloading: How Much Can WiFi Deliver? , 2013, IEEE/ACM Transactions on Networking.

[5]  Henrik Abrahamsson,et al.  Program popularity and viewer behaviour in a large TV-on-demand system , 2012, Internet Measurement Conference.

[6]  Aravind Srinivasan,et al.  Mobile Data Offloading through Opportunistic Communications and Social Participation , 2012, IEEE Transactions on Mobile Computing.

[7]  Vyas Sekar,et al.  Understanding the impact of video quality on user engagement , 2011, SIGCOMM.

[8]  Jeff Hunter,et al.  The carbon footprint of watching television, comparing digital terrestrial television with video-on-demand , 2011, Proceedings of the 2011 IEEE International Symposium on Sustainable Systems and Technology.

[9]  Sem C. Borst,et al.  Distributed Caching Algorithms for Content Distribution Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[10]  R.S. Tucker,et al.  Energy Consumption in Optical IP Networks , 2009, Journal of Lightwave Technology.

[11]  Minas Gjoka,et al.  Kangaroo: video seeking in P2P systems , 2009, IPTPS.

[12]  Danny De Vleeschauwer,et al.  Content storage architectures for boosted IPTV service , 2008, Bell Labs Technical Journal.

[13]  Pablo Rodriguez,et al.  Watching television over an IP network , 2008, IMC '08.

[14]  Cheng Huang,et al.  Challenges, design and analysis of a large-scale p2p-vod system , 2008, SIGCOMM '08.

[15]  Keith W. Ross,et al.  A Measurement Study of a Large-Scale P2P IPTV System , 2007, IEEE Transactions on Multimedia.

[16]  Cheng Huang,et al.  Can internet video-on-demand be profitable? , 2007, SIGCOMM '07.

[17]  Yung Ryn Choe,et al.  Improving VoD server efficiency with bittorrent , 2007, ACM Multimedia.

[18]  Siddhartha Annapureddy,et al.  Is high-quality vod feasible using P2P swarming? , 2007, WWW '07.

[19]  Ben Y. Zhao,et al.  Understanding user behavior in large-scale video-on-demand systems , 2006, EuroSys.

[20]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[21]  Susan Tyler Eastman,et al.  Media Programming: Strategies and Practices , 2005 .

[22]  Kamal Ali,et al.  TiVo: making show recommendations using a distributed collaborative filtering architecture , 2004, KDD.

[23]  Amin Vahdat,et al.  Bullet: high bandwidth data dissemination using an overlay mesh , 2003, SOSP '03.

[24]  Miguel Castro,et al.  SplitStream: high-bandwidth multicast in cooperative environments , 2003, SOSP '03.

[25]  Jussi Kangasharju,et al.  Object replication strategies in content distribution networks , 2002, Comput. Commun..

[26]  Vincent W. Freeh,et al.  Proceedings of the Sixth International Workshop on Web Caching and Content Distribution , 2001 .

[27]  Bulent Unel,et al.  DEPARTMENT OF ECONOMICS WORKING PAPER SERIES Effects of U.S. Banking Deregulation on Unemployment Dynamics , 2019 .

[28]  G. Linden,et al.  Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering , 2003, IEEE Distributed Syst. Online.