Du 09 au 12 décembre 2013

 

 

 

Dr Yassine Hadjadj-Aoul

   

Université d’Oran
Faculté des Sciences Exactes et Appliquées
Département Informatique

Laboratoire de Recherche en Informatique Industrielle et en Réseaux

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Pr Yassine HADJADJ-AOUL

Enseignant Chercheur à l’Université de Rennes I

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Biographie

Yassine HADJADJ AOUL (yhadjadj@irisa.fr) is currently working as an associate professor at the University of Rennes 1, France, where he is also a member of the IRISA Laboratory and the INRIA project-team Dionysos. He received a B.Sc. In computer engineering with high honours from Mohamed Boudiaf University, Oran, Algeria, in 1999. Dr. Hadjadj received his Master’s and Ph.D. degrees in computer science from the University of Versailles, France, in 2002 and 2007, respectively. He was an assistant professor at the University of Versailles from 2005 to 2007, where he was involved in several national and European projects such as NMS, IST-ATHENA, and IST-IMOSAN. He was also a post-doctoral fellow at the University of Lille 1 and a research fellow, under the EUFP6 EIF Marie Curie Action, at the National University of Dublin (UCD), where he was involved in the DOM’COM and IST-CARMEN projects, which aim at developing mixed Wi-Fi/WiMAX wireless mesh networks to support carrier grade services. His main research interests concern the fields of wireless networking, multimedia streaming architectures and protocols, congestion control protocols and QoS provisioning, and satellite and space communications. Dr. Hadjadj has been on the technical program committee of different IEEE conferences, including Globecom, ICC, VTC, PIMRC and IWCMC. His work on multimedia and wireless communications has led to more than 40 technical papers in journals and international conference proceedings.

Yassine HADJADJ AOUL, Maître de conférences depuis septembre 2009 à l’Université de Rennes 1 et membre depuis cette date de l’IRISA ainsi que de l’équipe projet INRIA Dionysos. Il a obtenu son diplôme d’ingénieur en Informatique à l’Université Mohamed Boudiaf (Oran, Algérie) en 1999, son Mastère recherche ainsi que sa thèse de doctorat en Réseaux Informatique de l’Université de Versailles en 2002 et 2007, respectivement. Après sa thèse de doctorat, Mr HADJADJ AOUL a obtenu un financement pour un post-doctorat à l’Université de Lille (USTL) au sein du laboratoire LIFL. Il a ensuite obtenu, suite à un concours, le financement européen Marie Curie de recherche sur deux ans qui lui a permis d’intégrer en tant que post-doctorant l’Université National de Dublin (UCD). Durant ces années de recherche Mr HADJADJ AOUL à travailler sur la transmission des flux multimédia dans les réseaux sans fil, il a aussi participé à différents projets nationaux et européens tels que : ANR NMS, IST-ATHENA, IST-IMOSAN et le projet FP7 sur les réseaux sans fil maillés CARMEN. Il coordonne aujourd’hui la contribution de l’INRIA dans le projet ANR VERSO ViPeer sur les réseaux de distribution de flux multimédia. Mr HADJADJ AOUL est auteur ou co-auteur de deux chapitres de livre, de 5 articles dans des revues internationales tels que : IEEE Wireless Communication Magazine, IEEE Transaction on Wireless Communications, Elsvier Computer Communication. Il a aussi publié plus 14 papiers dans des actes de conférences. Il est le responsable des modules sur les réseaux sans fil et le directeur de la formation continue EMiage à l’Université de Rennes 1.

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Vidéo Services

Video services are being adopted widely in both mobile and fixed networks. For their successful deployment, the content providers are increasingly becoming interested in evaluating the performance of such traffic from the final users’
perspective, that is, the Quality of Experience (QoE). However, subjective quality assessment methods are costly and can not be used in real time. Therefore, automatic estimation of QoE for video streaming is highly desired. In the work to be presented in the winter school, we talk about a proposed work on a no-reference QoE monitoring module for adaptive HTTP streaming using TCP and H.264 video codec. HTTP streaming using TCP is the popular choice of many web based and IPTV applications due to the intrinsic advantages of the protocol. Moreover, these applications do not suffer from video data loss due to the reliable nature of the transport layer. However, there can be playout interruptions and, in addition, if video bitrate adaptive streaming is used then the quality of video can vary due to lossy compression. Our QoE estimation module, based on Random Neural Network (RNN), models the impact of both factors.

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