Efficient and systematic network resource management

Abstract: The demand for network resources (e.g. forwarding capacity, buffer space) by increasingly used real-time multimedia applications is growing. Moreover, their stringent performance requirements (e.g. delay and jitter bounds) pose challenges on network resource management (RM). RM determines how available resources are modeled and distributed to achieve a performance goal such as assuring forwarding quality to real-time multimedia applications. Improvements to existing RM mechanisms can avoid performance limitations of networks by facilitating more efficient use of scarce resources. For example, in a vehicular to infrastructure (V2I) communication scenario that uses IP Multimedia Subsystem (IMS) lacking RM support for multicast, the 3G downlink quickly becomes a bottleneck although some information is addressed to multiple receivers. The main goal of this thesis is to develop RM algorithms and protocols that improve forwarding capacity utilization and remove performance bottlenecks. An additional goal is to improve the scalability of existing RM mechanisms. Three architectural paradigms are covered to demonstrate the advantages of efficient and systematic network RM: open access networks (OAN), next generation networks (NGN), and heterogeneous access networks (HAN). For OAN, a cross-layer signaling technique called parameter injection was developed. It reduces the signaling overhead and update time for real-time multimedia sessions over Wi-Fi while autonomously selecting the format and CODEC that best match the current resource settings. Within NGN, a resource management protocol is proposed for extending unicast signaling in IMS with multicast capabilities. The contribution uses adaptive and dynamic group size selection to improve resource utilization on the 3G downlink for the signaling and data planes. For HAN, an algorithm is proposed that predicts the best access network for achieving the highest QoE of a real-time multimedia session with the available QoS resources based on regression and statisticallearning. In all three paradigms, the provided core contributions serve the common goal of achieving a performance edge in terms of efficiency and systematic operation with a limited amount of network resources.