An autonomic architecture for optimizing QoE in multimedia access networks
The recent emergence of multimedia services, such as Broadcast TV and Video on Demand over traditional twisted pair access networks, has complicated the network management in order to guarantee a decent Quality of Experience (QoE) for each user. The huge amount of services and the wide variety of service specifics require a QoE management on a per-user and per-service basis. This complexity can be tackled through the design of an autonomic QoE management architecture. In this article, the Knowledge Plane is presented as an autonomic layer that optimizes the QoE in multimedia access networks from the service originator to the user. It autonomously detects network problems, e.g. a congested link, bit errors on a link, etc. and determines an appropriate corrective action, e.g. switching to a lower bit rate video, adding an appropriate number of FEC packets, etc. The generic Knowledge Plane architecture is discussed, incorporating the triple design goal of an autonomic, generic and scalable architecture. The viability of an implementation using neural networks is investigated, by comparing it with a reasoner based on analytical equations. Performance results are presented of both reasoners in terms of both QoS and QoE metrics.
knowledge plane neural networks qoe maximization