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A Social Relational Network-Based Architecture for Maintaining the Media Integrity and Optimizing the Quality of Experience: A Technical and Business Perspective

A Social Relational Network-Based Architecture for Maintaining the Media Integrity and Optimizing the Quality of Experience: A Technical and Business Perspective
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Author(s): Harilaos G. Koumaras (University of Bordeaux, France), Jose Oscar Fajardo (University of the Basque Country, Spain), Fidel Liberal (University of the Basque Country, Spain), Lingfen Sun (University of Plymouth, UK), Vaios Koumaras (Business College of Athens, Greece), Costas Troulos (PCN, Greece)and Anastasios Kourtis (National Centre for Scientific Research Demokritos, Greece)
Copyright: 2011
Pages: 22
Source title: Social Computing Theory and Practice: Interdisciplinary Approaches
Source Author(s)/Editor(s): Panagiota Papadopoulou (University of Athens, Greece), Panagiotis Kanellis (University of Athens, Greece)and Drakoulis Martakos (University of Athens, Greece)
DOI: 10.4018/978-1-61692-904-6.ch012

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Abstract

This chapter proposes a Content-aware and Network-aware Management System (CNMS) over a converged user-environment of social networking and mobile multimedia. The proposed CNMS will focus on applying dynamic personalized multi-layer adaptation for the optimization of the Quality of Experience (QoE) level in a requested media service according to the users’ preferences, favourites provided in their social network profile, and prior experiences rated by users themselves. By user’s preference extraction, a service/content classification will be performed according to an estimation of the user’s favourites, which will be used to provide optimized media delivery across the delivery chain. Therefore, the end-user will always receive her/his favourite service, like Internet Protocol Television (IPTV), Voice over Internet Protocol (VoIP), interactive application/on-line gaming, web browsing, at requested QoE. The system will ensure optimal allocation of network resources and optimal selection of streaming scheme according to different services/content types and user preferences, and therefore enhance the ratio of price-for-value for the specific subscription and achieve an end-to-end, holistic QoE optimisation. Although QoE is perceived as subjective, it is the only measure that counts for customers of a service. Being able to estimate the user preferences in a controlled manner through the end-user’s social networks profiles, helps operators understand what may be wrong with their services and their respective QoE. The proposed multimodal management system is user-centric and applies advanced machine learning techniques in order to extract user preferences from the social network profile of the user and build up a ranking scale of the services/contents. This ranking scale will be translated to adaptation actions per service type at several instances such as before the provision of the service takes place (i.e. Time Zero), during the delivering of the service (i.e. Time T), across all the network layers and delivery-chain nodes, while ensuring throughout the process that the main focus on the QoS-adaptation of the mobile access network is maintained.

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