• DocumentCode
    147842
  • Title

    Survey on machine learning-based QoE-QoS correlation models

  • Author

    Aroussi, Sana ; Mellouk, Abdelhamid

  • Author_Institution
    Comput. Sci. Dept., Dahlab Univ., Algiers, Algeria
  • fYear
    2014
  • fDate
    27-29 April 2014
  • Firstpage
    200
  • Lastpage
    204
  • Abstract
    The machine learning provides a theoretical and methodological framework to quantify the relationship between user OoE (Quality of Experience) and network QoS (Quality of Service). This paper presents an overview of QoE-QoS correlation models based on machine learning techniques. According to the learning type, we propose a categorization of correlation models. For each category, we review the main existing works by citing deployed learning methods and model parameters (QoE measurement, QoS parameters and service type). Moreover, the survey will provide researchers with the latest trends and findings in this field.
  • Keywords
    learning (artificial intelligence); quality of experience; quality of service; telecommunication computing; QoE measurement; QoE-QoS correlation model; QoS parameter; QoS service type; machine learning; quality of experience; quality of service; Correlation; Data models; Packet loss; Predictive models; Quality of service; Streaming media; Correlation model; Machine Learning; Quality of Experience; Quality of Service;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing, Management and Telecommunications (ComManTel), 2014 International Conference on
  • Conference_Location
    Da Nang
  • Print_ISBN
    978-1-4799-2904-7
  • Type

    conf

  • DOI
    10.1109/ComManTel.2014.6825604
  • Filename
    6825604