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User-Based Predictive Caching of Streaming Media
Linköping University, Department of Computer and Information Science.
Linköping University, Department of Computer and Information Science.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Användarbaserad predektiv cachning av strömmande media (Swedish)
Abstract [en]

Streaming media is a growing market all over the world which sets a strict requirement on mobile connectivity. The foundation for a good user experience when supplying a streaming media service on a mobile device is to ensure that the user can access the requested content. Due to the varying availability of mobile connectivity measures has to be taken to remove as much dependency as possible on the quality of the connection. This thesis investigates the use of a Long Short-Term Memory machine learning model for predicting a future geographical location for a mobile device. The predicted location in combination with information about cellular connectivity in the geographical area is used to schedule prefetching of media content in order to improve user experience and to reduce mobile data usage. The Long Short-Term Memory model suggested in this thesis achieves an accuracy of 85.15% averaged over 20000 routes and the predictive caching managed to retain user experience while decreasing the amount of data consumed.

Place, publisher, year, edition, pages
2018. , p. 58
Keywords [en]
cache, software, media, streaming, ml, ai, machine learning, LSTM, GRU, network, coverage
National Category
Software Engineering Computer Sciences Information Systems
Identifiers
URN: urn:nbn:se:liu:diva-151008ISRN: LIU-IDA/LITH-EX-A--18/033—SEOAI: oai:DiVA.org:liu-151008DiVA, id: diva2:1247036
Subject / course
Information Technology
Presentation
2018-06-18, John von Neumann, Linköping, 10:15 (English)
Supervisors
Examiners
Note

This thesis is written as a joint thesis between two students from different universities. This means the exact same thesis is published at two universities (LiU and KTH) but with different style templates. The other report has identification number: TRITA-EECS-EX-2018:403

Available from: 2018-09-14 Created: 2018-09-10 Last updated: 2018-09-14Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf