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Towards network-wide QoE fairness using openflow-assisted adaptive video streaming

Published:16 August 2013Publication History

ABSTRACT

Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network.

References

  1. S. Akhshabi, L. Anantakrishnan, C. Dovrolis, and A. Begen. What Happens When HTTP Adaptive Streaming Players Compete for Bandwidth? In Proc. NOSSDAV, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Akhshabi, A. Begen, and C. Dovrolis. An Experimental Evaluation of Rate-adaptation Algorithms in Adaptive Streaming over HTTP. In Proc. 2nd annual ACM Conference on Multimedia Systems, MMSys '11, pages 157--168, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. G. Cermak, M. Pinson, and S. Wolf. The Relationship Among Video Quality, Screen Resolution, and Bit Rate. IEEE Trans. Broadcast., 57:258--262, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  4. CISCO. The Zettabyte Era. Technical report, 2012.Google ScholarGoogle Scholar
  5. R. J. Dakin. A Tree-search Algorithm for Mixed Integer Programming Problems. Comput. J., 8(3):250--255, 1965.Google ScholarGoogle ScholarCross RefCross Ref
  6. DASH-JS: A JavaScript-based DASH library for Google Chrome. http://www-itec.uni-klu.ac.at/dash/?page_id=746.Google ScholarGoogle Scholar
  7. F. Dobrian, A. Awan, D. Joseph, A. Ganjam, J. Zhan, V. Sekar, I. Stoica, and H. Zhang. Understanding the Impact of Video Quality on User Engagement. SIGCOMM Computer Communication Review, 41(4):362--373, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Y. Elkhatib. Monitoring, Analysing and Predicting Network Performance in Grids. PhD thesis, Lancaster University, Sep. 2011.Google ScholarGoogle Scholar
  9. J. Esteban, S. Benno, A. Beck, Y. Guo, V. Hilt, and I. Rimac. Interactions Between HTTP Adaptive Streaming and TCP. In Proc. NOSSDAV, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Gettys and K. Nichols. Bufferbloat: Dark Buffers in the Internet. ACM Queue, 9(11):40--54, Nov. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. O. Goga and R. Teixeira. Speed Measurements of Residential Internet Access. In Proc. Passive and Active Measurement, pages 168--178, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T.-Y. Huang, N. Handigol, B. Heller, N. McKeown, and R. Johari. Confused, Rimid, and Unstable: Picking a Video Streaming Rate is Hard. In Proc. ACM IMC, pages 225--238, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. ISO-IEC 23009--1:2012 Information Technology. Dynamic Adaptive Streaming over HTTP (DASH).Google ScholarGoogle Scholar
  14. J. Jiang, V. Sekar, and H. Zhang. Improving Fairness, Efficiency, and Stability in HTTP-based Adaptive Video Streaming with FESTIVE. In Proc. ACM CoNEXT, pages 97--108, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. X. Liu, F. Dobrian, H. Milner, J. Jiang, V. Sekar, I. Stoica, and H. Zhang. A Case for a Coordinated Internet Video Control Plane. In Proc. ACM SIGCOMM 2012 on Applications, Technologies, Architectures and Protocols for Computer Communication, pages 359--370, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. A. Mansy, B. Ver Steeg, and M. Ammar. SABRE: A Client based Technique for Mitigating the Buffer Bloat Effect of Adaptive Video Flows. In Proc. 3rd annual ACM Conference on Multimedia Systems, MMSys '12. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner. OpenFlow: Enabling Innovation in Campus Networks. SIGCOMM Computer Communication Review, 38(2):69--74, Mar. 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. R. Mok, X. Luo, E. Chan, and R. Chang. QDASH: a QoE-aware DASH system. In Proc. 3rd annual ACM Conference on Multimedia Systems, MMSys '12, pages 11--22, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. M. Mu, W. Knowles, P. Georgopoulos, S. Simpson, E. Cerqueira, N. Race, A. Mauthe, and D. Hutchison. Quality Evaluation in Peer-to-Peer IPTV Services. In Data Traffic Monitoring and Analysis: From Measurement, Classification and Anomaly Detection to Quality of Experience, LNCS, pages 302--319. Springer, 2013.Google ScholarGoogle Scholar
  20. Ofcom. Overview of UK Broadband Speeds. http://stakeholders.ofcom.org.uk/market-data-research/other/telecoms-research/broadband-speeds/bb-speeds-nov-11.Google ScholarGoogle Scholar
  21. G. Tian and Y. Liu. Towards Agile and Smooth Video Adaptation in Dynamic HTTP Streaming. In Proc. ACM CoNEXT, pages 109--120, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. I. T. Union. H.264: Advanced Video Coding for Generic Audiovisual Services (Part 10), 2003.Google ScholarGoogle Scholar
  23. Z. Wang, L. Lu, and A. C. Bovik. Video Quality Assessment based on Structural Distortion Measurement. Signal Processing: Image Communication, 19(2):121--132, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  24. S. Wolf and M. H. Pinson. Spatial-temporal Distortion Metric for in-service Quality Monitoring of any Digital Video System. In Proc. SPIE, volume 3845, page 266, 1999.Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Conferences
      FhMN '13: Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking
      August 2013
      68 pages
      ISBN:9781450321839
      DOI:10.1145/2491172

      Copyright © 2013 ACM

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      Publication History

      • Published: 16 August 2013

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      FhMN '13 Paper Acceptance Rate9of20submissions,45%Overall Acceptance Rate9of20submissions,45%

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