A New Scheme for QoE Management of Live Video Streaming in Cloud Environment

  • Dheyaa Jasim Kadhim
  • Xinguo Yu
  • Saba Qasim Jabbar
  • Yu Li
  • Wenxing Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)


Live video streaming process consumes very large data storage and takes very long time, so it requires big data storage and computing infrastructures for implementation. Accordingly, the use of cloud computing is becoming a common practice solution for streaming service providers. This work proposes a new scheme to manage the quality of experience (QoE) for live video streaming viewers, aimed directly at cloud computing environments. This scheme proposes to make optimal usage of cloud computing resources and quality services to meet the quality of experience (QoE) requirements of the live video streaming viewers without considering another cost to the video service provider. We examine the user’s quality of experience using dynamic adaptive streaming HTTP (DASH) technique. Then, we present and derive three important performance indicators which effect on viewer’s QoE namely: startup delay, deadline time (time nulling including null duration and number of null time), and bit rate level variations. The simulation results show that the tested indication parameters do not need to access the service providers in order to manage QoE of viewers neither do not need to insert them into the video streaming client software to determine the user experience in live video streaming. So, we believe that our proposed scheme and the performance indicators that studied in our work can serve as useful and light-weight tools for live video streaming service provider to monitor and control their quality of services.


Cloud computing Live video streaming QoE management DASH 


  1. 1.
    Hobfeld, T., Schatz, R., Varela, M., Timmerer, C.: Challenges of QoE management for cloud applications. IEEE Commun. Mag. 50(4), 28–36 (2012)CrossRefGoogle Scholar
  2. 2.
    De Cicco, L., Mascolo, S., Palmisano, V.: Feedback control for adaptive live video streaming. In: Proceedings of the Second Annual ACM Conference on Multimedia Systems, pp. 145–156. ACM (2011)Google Scholar
  3. 3.
    Vetro, A., Christopoulos, C., Sun, H.: Video transcoding architectures and techniques: an overview. IEEE Sig. Process. Mag. 20(2), 18–29 (2003)CrossRefGoogle Scholar
  4. 4.
    Cheng, X., Liu, J., Dale, C.: Understanding the characteristics of internet short video sharing: a YouTube-based measurement study. IEEE Trans. Multimed. 15(5), 1184–1194 (2013)CrossRefGoogle Scholar
  5. 5.
    Jokhio, F., Deneke, T., Lafond, S., Lilius, J.: Analysis of video segmentation for spatial resolution reduction video transcoding. In: International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), pp. 1–6. IEEE (2011)Google Scholar
  6. 6.
    Kafetzakis, E., Koumaras, H., Kourtis, M., Koumaras, V.: QoE4CLOUD: a QoE-driven multidimensional framework for cloud environments. In: International Conference on Telecommunications and Multimedia (TEMU), pp. 77–82. IEEE (2012)Google Scholar
  7. 7.
    Qian, H., Medhi, D., Trivedi, K.: A hierarchical model to evaluate quality of experience of online services hosted by cloud computing. In: International Symposium on Integrated Network Management (IM), pp. 105–111. IEEE (2011)Google Scholar
  8. 8.
    Wang, F., Liu, J., Chen, M.: CALMS: cloud-assisted live media streaming for globalized demands with time/region diversities. In: Proceedings of INFOCOM, pp. 199–207. IEEE (2012)Google Scholar
  9. 9.
    Liu, Y., Dey, S., Gillies, D., Ulupinar, F., Luby, M.: User experience modeling for DASH video. In: 20th International Workshop in Packet Video Workshop (PV), pp. 1–8. IEEE (2013)Google Scholar
  10. 10.
    Pinson, M.H., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Trans. Broadcast. 50(3), 312–322 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Engineering Research Center for E-Learning, CCNUWuhanChina
  2. 2.Huazhong University of Science and TechnologyWuhanChina
  3. 3.GuiZhou Vocational Technology College of Electronics and InformationKailiChina

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