The proliferation of multimedia services over access networks (e.g., IPTV or network-based Personal Video Recording) has introduced important new revenue potential for network and service providers but has also complicated the management burden. As a result, today's management of multimedia networks is often too static to cope with the increasing quality requirements of multimedia services. A key point in these quality requirements is the quality as perceived by the end users, denoted as the Quality of Experience (QoE). In the thesis, we have introduced an auto-nomic management layer that optimizes the QoE of multimedia networks. We have studied several QoE optimizing techniques with respect to traffic adaptation, admission control and video rate adaptation. All these QoE optimizing techniques exhibit autonomic behavior as they continuously monitor the network to optimize their configuration and consequently optimize the QoE. Furthermore, we have investigated the coordinated deployment of these QoE optimizing techniques by focusing on the exchange of context between entities in the distributed autonomic management layer. Through extensive evaluation using both simulation and emulation on a large-scale testbed, we have shown that the proposed QoE optimizing techniques can successfully optimize the QoE of multimedia services. This QoE optimization was characterized in terms of metrics such as the number of admitted sessions and video quality.
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