Take-away TV: Recharging Work commutes with predictive preloading of catch-Up TV content

Dmytro Karamshuk*, Nishanth Sastry, Mustafa Al-Bassam, Andrew Secker, Jigna Chandaria

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

Mobile data offloading can greatly decrease the load on and usage of current and future cellular data networks by exploiting opportunistic and frequent access to Wi-Fi connectivity. Unfortunately, Wi-Fi access from mobile devices can be difficult during typical work commutes, e.g., via trains or cars on highways. In this paper, we propose a new approach: to preload the mobile device with content that a user might be interested in, thereby avoiding the need for cellular data access. We demonstrate the feasibility of this approach by developing a supervised machine learning model that learns from user preferences for different types of content, and propensity to be guided by the user interface of the player, and predictively preload entire TV shows. Testing on a data set of nearly 3.9 million sessions from all over the U.K. to BBC TV shows, we find that predictive preloading can save over 71% of the mobile data for an average user.

Original languageEnglish
Article number7485863
Pages (from-to)2091-2101
Number of pages11
JournalIEEE Journal on Selected Areas in Communications
Volume34
Issue number8
Early online date6 Jun 2016
DOIs
Publication statusPublished - 1 Aug 2016

Keywords

  • catch-up TV
  • content delivery
  • mobile prefetching
  • predictive preloading
  • supervised learning
  • Video streaming

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