Intelligent and adaptive middleware to improve user-perceived QoS in multimedia applications

We investigate the use of adaptive multimedia applications to improve the user-perceived QoS in next-generation wireless networks. These networks will be characterized by the heterogeneity of the access technologies including ad hoc wireless network extensions. In these network scenarios in which the links are error-prone, their capacity is extremely variable and even the topology can change due to handovers and mobility of the nodes, it is unrealistic to think of traditional network-layer QoS mechanisms alone, as a means to offer strict end-to-end QoS guarantees. As a complement to the network-layer QoS protocols, this paper presents an intelligent adaptation middleware allowing multimedia applications to dynamically adapt their internal settings. This intelligence enables the applications to minimize the impact of the adverse and changing network conditions in the QoS level perceived by the user. We use a machine learning technique to model the user's perception of QoS from a large set of QoS scores given by real users. We present some results demonstrating that our proposal clearly outperforms traditional multimedia internetworking.