skip to main content
10.1145/2680821.2680836acmconferencesArticle/Chapter ViewAbstractPublication PagesconextConference Proceedingsconference-collections
research-article

Location-Based Adaptation for DASH in Vehicular Environment

Published:02 December 2014Publication History

ABSTRACT

In dynamic adaptive streaming over HTTP (DASH), a video file is encoded at different bitrates and segmented into small chunks. Each time, the most appropriate bitrate will be selected for a video chunk based on the network condition observed by streaming client. This may lead to video freezing or not efficiently using the network capacity due to unexpected bandwidth changes especially in fast moving environment. In this research we take advantage of geographical knowledge of bandwidth to predict the network behavior in next moments. Using simulation driven by real-world 3G bandwidth and vehicular mobility traces we achieved higher QoS when geographical throughput history was considered.

References

  1. Adobe. HTTP Dynamic Streaming on the Adobe Flash Platform. {Online accessed 04-March-2013}, URL: http://www.adobe.com/au/products/hdsdynamic- streaming.html.Google ScholarGoogle Scholar
  2. Apple. HTTP Live Streaming Overview. {Online accessed 01-October-2014}, URL: http://goo.gl/CElrwV.Google ScholarGoogle Scholar
  3. BlenderFoundation. Big Buck Bunny. {Online accessed 04-March-2013}, URL: http://www.bigbuckbunny.org.Google ScholarGoogle Scholar
  4. A. Bokani, M. Hassan, and S. Kanhere. HTTP-based Adaptive Streaming for Mobile Clients using Markov Decision Process. In Packet Video Workshop (PV), San Jose, USA. 12 December 2013.Google ScholarGoogle ScholarCross RefCross Ref
  5. P. Deshpande, X. Hou, and S. R. Das. Performance Comparison of 3G and Metro-Scale WiFi for Vehicular Network Access. In Proceedings of the 10th ACM conference on Internet measurement, Melbourne, Australia. 1--3 November 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Jarnikov and T. Ozcelebi. Client Intelligence for Adaptive Streaming Solutions. Signal Processing: Image Communication, 26(7):378--389, 2011.Google ScholarGoogle Scholar
  7. C. Liu, I. Bouazizi, and M. Gabbouj. Rate Adaptation for Adaptive HTTP Streaming. In Proceedings of the second annual ACM conference on Multimedia systems (MMSys'11), New York, USA. 23 February 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Y. Qiao, J. Skicewicz, and P. Dinda. An Empirical Study of the Multiscale Predictability of Network Traffic. In Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing (HPDC-13), Honolulu, Hawaii, USA. June 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. T. Stockhammer. Dynamic Adaptive Streaming Over HTTP: Standards and Design Principles. In Proceedings of the second annual ACM conference on Multimedia systems (MMSys), San Jose, USA. 23--25 February 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. C. C. Wüst and W. F. Verhaegh. Quality Control for Scalable Media Processing Applications. Journal of Scheduling, 7(2):105--117, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. Yao, S. S. Kanhere, and M. Hassan. An Empirical Study of Bandwidth Predictability in Mobile Computing. In Proceedings of the third ACM international workshop on Wireless network testbeds, experimental evaluation and characterization (MobiCom-WiNTECH), San Francisco, USA. 14--19 September 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. J. Yao, S. S. Kanhere, and M. Hassan. Improving QoS in High-Speed Mobility Using Bandwidth Maps. IEEE Transactions on Mobile Computing, 11(4):603--617,= 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Zambelli. IIS smooth streaming technical overview. Microsoft Corporation, 3, 2009.Google ScholarGoogle Scholar

Index Terms

  1. Location-Based Adaptation for DASH in Vehicular Environment

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CoNEXT Student Workshop '14: Proceedings of the 2014 CoNEXT on Student Workshop
      December 2014
      58 pages
      ISBN:9781450332828
      DOI:10.1145/2680821

      Copyright © 2014 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 December 2014

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      CoNEXT Student Workshop '14 Paper Acceptance Rate17of34submissions,50%Overall Acceptance Rate198of789submissions,25%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader