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VAS: a video adaptation service to support mobile video

Published:18 March 2015Publication History

ABSTRACT

Even though cellular networks offer a ubiquitous access to the Internet for mobile devices, their throughput is often insufficient for the rising demand for mobile video. Classical video streaming approaches can not cope with bandwidth fluctuations common in those networks. As a result adaptive approaches for video streaming have been proposed and are increasingly adopted on mobile devices. However, existing adaptive video systems often rely on available network resources alone. As video content properties have a large influence on the perception of occurring quality adaptations our belief is that this is not sufficient. In this work, we thus present a support service for a content-aware video adaptation on mobile devices. Based on the actual video content the adaptation process is improved for both the available network resources and the perception of the user. By leveraging the content properties of a video stream, the system is able to keep a stable video quality and at the same time reduce the network load.

References

  1. V. Adzic, H. Kalva, and B. Furht. Optimizing video encoding for adaptive streaming over HTTP. IEEE Transactions on Consumer Electronics, 2012.Google ScholarGoogle Scholar
  2. E. Akyol, A. M. Tekalp, and M. R. Civanlar. Content-Aware Scalability-Type Selection for Rate Adaptation of Scalable Video. EURASIP Journal on Advances in Signal Processing, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. P. M. Eittenberger, M. Hamatschek, M. Großmann, and U. R. Krieger. Monitoring mobile video delivery to Android devices. In ACM Multimedia Systems Conference, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Fiandrotti, D. Gallucci, E. Masala, and J. De Martin. Content-adaptive traffic prioritization of spatio-temporal scalable video for robust communications over QoS-provisioned 802.11e networks. Signal Processing: Image Communication, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. ITU. ITU-R Recommendation P.910, 2008.Google ScholarGoogle Scholar
  6. S. Lederer, C. Mueller, B. Rainer, M. Waltl, and C. Timmerer. An open source MPEG DASH evaluation suite. In IEEE Conference on Visual Communications and Image Processing, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  7. Z. Li, A. C. Begen, J. Gahm, Y. Shan, B. Osler, and D. Oran. Streaming video over HTTP with consistent quality. In ACM Multimedia Systems Conference, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. K. P. Mok, X. Luo, E. W. W. Chan, and R. K. C. Chang. QDASH: A QoE-aware DASH system. In ACM Multimedia Systems Conference, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. A. K. Moorthy, L. K. Choi, and A. C. Bovik. Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies. IEEE Journal of Selected Topics in Signal Processing, 2012.Google ScholarGoogle Scholar
  10. M. Pinson and S. Wolf. A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting, 50(3), 2004.Google ScholarGoogle ScholarCross RefCross Ref
  11. H. Schwarz, D. Marpe, and T. Wiegand. Overview of the Scalable Video Coding Extension of the H.264/AVC Standard. IEEE Transactions on Circuits and Systems for Video Technology, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. Stockhammer. Dynamic Adaptive Streaming over HTTP: Standards and Design Principles. In ACM Multimedia Systems Conference, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. X. Wang, M. Chen, T. T. Kwon, L. Yang, and V. C. M. Leung. AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds. IEEE Transactions on Multimedia, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. R. Zabih, J. Miller, and K. Mai. A feature-based algorithm for detecting and classifying production effects. Multimedia systems, 7(2), 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M. Zink, O. Künzel, J. Schmitt, and R. Steinmetz. Subjective impression of variations in layer encoded videos. In International Conference on Quality of Service. Springer, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. T. Zinner, O. Hohlfeld, O. Abboud, and T. Hossfeld. Impact of frame rate and resolution on objective QoE metrics. In International Workshop on Quality of Multimedia Experience, 2010.Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Conferences
      NOSSDAV '15: Proceedings of the 25th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video
      March 2015
      83 pages
      ISBN:9781450333528
      DOI:10.1145/2736084

      Copyright © 2015 ACM

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      New York, NY, United States

      Publication History

      • Published: 18 March 2015

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      NOSSDAV '15 Paper Acceptance Rate12of43submissions,28%Overall Acceptance Rate118of363submissions,33%

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