Quality of experience-driven adaptation scheme for video applications over wireless networks

Quality of experience-driven adaptation scheme for video applications over wireless networks

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

User's perceived quality of service (QoS) or quality of experience (QoE) is likely to be the major determining factor in the success of new multimedia applications over wireless/mobile networks. The primary aim of this study is to present an adaptation scheme that is QoE-driven for optimising content provisioning and network resource utilisation for video applications over wireless networks. The proposed scheme encompasses the application of a QoE-driven model for optimising content provisioning and network resource utilisation. The content provisioning is optimised by the determination of initial content quality by adapting the video sender bitrate (SBR) according to users' QoE requirement. By finding the impact of the QoS parameters on end-to-end perceptual video quality, the optimum trade-off between SBR and frame rate is found and the benefits to network providers in maximising existing network resources are demonstrated. The QoE is measured in terms of the mean opinion score. The proposed scheme makes it possible for content providers to achieve optimum streaming (with an appropriate SBR) suitable for the network and content type for a requested QoE. The scheme is also beneficial for network providers for network resource provision and planning, and therefore maximising existing network infrastructure by providing service differentiation.


    1. 1)
      • ITU-T. Rec P.800: ‘Methods for subjective determination of transmission quality’, 1996.
    2. 2)
      • Khan, A., Sun, L., Ifeachor, E.: `Content clustering based video quality prediction model for MPEG4 video streaming over wireless networks', IEEE ICC Conf., 14–18 June 2009, Dresden, Germany.
    3. 3)
      • Y. Wang , M. Schaar , A. Loui . Classification-based multidimensional adaptation prediction for scalable video coding using subjective quality evaluation. IEEE Trans. Circuits Syst. Video Technol. , 10 , 1270 - 1279
    4. 4)
      • Cranley, N., Murphy, L., Perry, P.: `Content-based adaptation of streamed multimedia', IEEE Int. Conf. on Management of Multimedia Networks and Services, No. 7, 3–6 October 2004, San Diego, CA.
    5. 5)
      • H. Koumaras , A. Kourtis , C. Lin , C. Shieh . End-to-end prediction model of video quality and decodable frame rate for MPEG broadcasting services. Int. J. Adv. Netw. Services , 1 , 19 - 29
    6. 6)
      • Onur, O., Alatan, A.: `Video adaptation based on content characteristics and hardware capabilities', Second Int. Workshop on Semantic Media Adaptation and Personalization, 2007.
    7. 7)
      • Manzato, M., Goularter, R.: `Live video adaptation: a context-aware approach', ACM Proc. 11th Brazilian Symp. on Multimedia and the Web, 2005.
    8. 8)
      • G. Zhai , J. Cai , W. Lin , X. Yang , W. Zhang . Three-dimensional scalable video adaptation via user-end perceptual quality assessment. IEEE Trans. Broadcast. (Special Issue on Qual. Issues Multimed. Broadcast.) , 3 , 719 - 727
    9. 9)
      • Q. Huynh-Thu , M. Ghanbari . Temporal aspect of perceived quality in mobile video broadcasting. IEEE Trans. Broadcast. , 3 , 641 - 651
    10. 10)
      • Agboma, F., Liotta, A.: `QoE-aware QoS management', Sixth Int. Conf. on Advances in Mobile computing and Multimedia, 24–26 November 2008.
    11. 11)
      • P. Calyam , E. Ekicio , C. Lee , M. Haffner , N. Howes . A GAP-model based framework for online VVoIP QoE measurement. J. Commun. Netw. , 4 , 446 - 56
    12. 12)
      • F. Angelis , I. Habib , F. Davide , M. Naghshineh . Intelligent content aware services in 3G wireless networks. IEEE J. Sel. Areas Commun. , 2 , 221 - 234
    13. 13)
      • Papadimitriou, P., Tsaoussidis, V.: `A rate control scheme for adaptive video streaming over the internet', IEEE ICC, 2007.
    14. 14)
      • Alexiou, A., Bouras, C., Igglesis, V.: `A decision feedback scheme for multimedia transmission over 3G mobile networks', WOCN, 2005, Dubai, UAE.
    15. 15)
      • H. Garudadri , H. Chung , N. Srinivasamurthy , P. Sagetong . Rate adaptation for video telephony in 3G networks. Packet Video , 342 - 348
    16. 16)
    17. 17)
      • D. Kim , K. Jun . Dynamic bandwidth allocation scheme for video streaming in wireless cellular networks. IEICE Trans. Commun. , 2 , 350 - 356
    18. 18)
      • Klaue, J., Tathke, B., Wolisz, A.: `Evalvid – a framework for video transmission and quality evaluation', Proc. 13th Int. Conf. on Modelling Techniques and Tools for Computer Performance Evaluation, 2003, Urbana, IL, USA, p. 255–272.
    19. 19)
      • W.J. Krzanowski . (1988) Principles of multivariate analysis.
    20. 20)
      • Khan, A., Sun, L., Ifeachor, E.: `Impact of video content on video quality for video over wireless networks', Fifth ICAS, 20–25 April 2009, Valencia, Spain.
    21. 21)
      • Khan, A., Sun, L., Ifeachor, E.: `Content classification based and QoE-driven video send bitrate adaptation scheme', Fifth Int. Mobimedia Conf., 7–9 September 2009, London, UK.
    22. 22)
      • Telchemy application notes: ‘Understanding of video quality metrics’. Telchemy, February 2008.
    23. 23)
      • NS2,
    24. 24)
      • Ffmpeg,

Related content

This is a required field
Please enter a valid email address