Article

Addressing user expectations in mobile content delivery.

Mobile Information Systems 01/2007; 3:153-164.
Source: DBLP
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    Conference Proceeding: Online QoE prediction
    V. Menkovski, G. Exarchakos, A. Liotta
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    ABSTRACT: The Quality of Experience (QoE) is an irreplaceable metric for evaluating the perceived quality of consumers of multimedia content. Due to the subjectiveness of QoE the most suitable way to measure it is by executing subjective studies. However, executing subjective studies is a complex and expensive process. Careful recreation of the viewing conditions is necessary, and a strict selection of the test subjects is required based on many criteria. This is why solutions are often found in various objective methodologies for measuring the QoE of multimedia. These solutions even though more practical are less accurate and cannot reflect the user expectations. In this work we present a method for building QoE prediction models using machine learning techniques from continuous real-time customer feedback, i.e., during the service execution. This online learning approach builds and adapts prediction models that estimate the QoE based on given Quality of Service metrics from real-time user feedback and does not require apriori execution of subjective studies.
    Quality of Multimedia Experience (QoMEX), 2010 Second International Workshop on; 07/2010
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    Article: Adaptive Broadband Wideo Streaming for IPTV Wireless Access.
    Salah S. Al-Majeed, Martin Fleury
    J. Mobile Multimedia. 01/2011; 7:177-193.
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    Conference Proceeding: Machine Learning Approach for Quality of Experience Aware Networks
    V. Menkovski, G. Exarchakos, A. Liotta
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    ABSTRACT: Efficient management of multimedia services necessitates the understanding of how the quality of these services is perceived by the users. Estimation of the perceived quality or Quality of Experience (QoE) of the service is a challenging process due to the subjective nature of QoE. This process usually incorporates complex subjective studies that need to recreate the viewing conditions of the service in a controlled environment. In this paper we present Machine Learning techniques for modeling the dependencies of different network and application layer quality of service parameters to the QoE of network services using subjective quality feedback. These accurate QoE prediction models allow us to further develop a geometrical method for calculating the possible remedies per network stream for reaching the desired level of QoE. Finally we present a set of possible network techniques that can deliver the desired improvement to the multimedia streams.
    Intelligent Networking and Collaborative Systems (INCOS), 2010 2nd International Conference on; 12/2010
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