The third generation of mobile communication systems introduce interactive Multicast and Unicast multimedia services at a fast data rate of up to 2 Mbps and is expected to complete the globalization of the mobile telecommunication systems. The implementation of these services on satellite systems, particularly for broadcast and multicast applications to complement terrestrial services is ideal since satellite systems are capable of providing global coverage in areas not served by terrestrial telecommunication services. However, the main bottleneck of such systems is the scarcity of radio resources for supporting multimedia applications which has resulted in the rapid growth in research efforts for deriving efficient radio resource management techniques. This issue is addressed in this thesis, where the main emphasis is to design a dynamic scheduling framework and algorithm that can improve the overall performance of the radio resource management strategy of a UMTS compatible satellite network, taking into account the unique characteristics of wireless channel conditions. This thesis will initially be focused on the design of the network and functional architecture of a UMTS -compatible satellite network. Based on this architecture, an effective scheduling framework is designed, which can provide different types of resource assigning strategies. A functional model of scheduler is defined to describe the behaviours and interactions between different functional entities. An OPNET simulation model with a complete network protocol stack is developed to validate the performance of the scheduling algorithms implemented in the satellite network. Different types of traffic are considered for the OPNET simulation, such as the Poisson Process, ONOFF Source and Self Similar Process, so that the performance of scheduling algorithm can be analyzed for different types of services. A novel scheduling algorithm is proposed to optimise the channel utilisation by considering the characteristics of the wireless channel, which are bursty and location dependent. In order to overcome the channel errors, different code rates are applied for the user under different channel conditions. The proposed scheduling algorithm is designed to give higher priority to users with higher code rate, so that the throughput of network is optimized and at the same time, maintaining the end users' service level agreements. The fairness of the proposed scheduling algorithm is validated using OPNET simulation. The simulation results show that the algorithm can fairly allocate resource to different connections not only among different service classes but also within the same service class depending on their QoS attributes.

Le conflit entre la demande de services multimédia en multidiffusion à haut débit (MBMS) et les limites en ressources radio demandent une gestion efficace de l'allocation des ressources radio (RRM) dans les réseaux 3G UMTS. À l'opposé des travaux existant dans ce domaine, cette thèse se propose de résoudre le problème de RRM dans les MBMS par une approche d’optimisation combinatoire. Le travail commence par une modélisation formelle du problème cible, désigné comme Flexible Radio Resource Management Model (F2R2M). Une analyse de la complexité et du paysage de recherche est effectuée à partir de ce modèle. Tout d’abord on montre qu'en assouplissant les contraintes de code OVSF, le problème de RRM pour les MBMS peut s'apparenter à un problème de sac à dos à choix multiples (MCKP). Une telle constatation permet de calculer les limites théoriques de la solution en résolvant le MCKP similaire. En outre, l'analyse du paysage montre que les espaces de recherche sont accidentés et constellés d'optima locaux. Sur la base de cette analyse, des algorithmes métaheuristiques sont étudiés pour résoudre le problème. Nous montrons tout d'abord que un Greedy Local Search (GLS) et un recuit simulé (SA) peuvent trouver de meilleures solutions que les approches existantes implémentées dans le système UMTS, mais la multiplicité des optima locaux rend les algorithmes très instables. Un algorithme de recherche tabou (TS) incluant une recherche à voisinage variable (VNS) est aussi développé et comparé aux autres algorithmes (GLS et SA) et aux approches actuelles du système UMTS ; les résultats de la recherche tabou dépassent toutes les autres approches. Enfin les meilleures solutions trouvées par TS sont également comparées avec les solutions théoriques générées par le solveur MCKP. On constate que les meilleures solutions trouvées par TS sont égales ou très proches des solutions optimales théoriques. / The high throughputs supported by the multimedia multicast services (MBMS) and the limited radio resources result in strong requirement for efficient radio resource management (RRM) in UMTS 3G networks. This PhD thesis proposes to solve the MBMS RRM problem as a combinatorial optimization problem. The work starts with a formal modeling of the problem, named as the Flexible Radio Resource Management Model (F2R2M). An in-depth analysis of the problem complexity and the search landscape is done from the model. It is showed that, by relaxing the OVSF code constraints, the MBMS RRM problem can be approximated as a Multiple-Choice Knapsack Problem (MCKP). Such work allows us to compute the theoretical solution bounds by solving the approximated MCKP. Then the fitness landscape analysis shows that the search spaces are rough and reveal several local optimums. Based on the analysis, some metaheuristic algorithms are studied to solve the MBMS RRM problem. We first show that a Greedy Local Search (GLS) and a Simulated Annealing (SA) allow us to find better solutions than the existing approaches implemented in the UMTS system, however the results are instable due to the landscape roughness. Finally we have developed a Tabu Search (TS) mixed with a Variable Neighborhood Search (VNS) algorithm and we have compared it with GLS, SA and UMTS embedded algorithms. Not only the TS outperforms all the other approaches on several scenarios but also, by comparing it with the theoretical solution bounds generated by the MCKP solver, we observe that TS is equal or close to the theoretical optimal solutions.