No abstract

We propose a new way of architecting the wireless multimedia communications systems by jointly optimizing the protocol stack at each station and the resource exchanges among stations. We model wireless stations as rational players competing for available wireless resources in a dynamic repeated game. We investigate and quantify the system performance and the impact of different crosslayer strategies deployed by wireless stations onto their own performance as well as the competing station performance. We show through simulations that the proposed game-theoretic resource management outperforms alternative techniques such as air-fair time and equal time resource allocation in terms of the total system utility.

Three-dimensional (3D) wavelet coding schemes have been demonstrated to be efficient techniques for video compression applications. The scalability property of such schemes is one of the most important issues for video surveillance systems. In this paper we introduce a new lifting-based method of temporal decomposition which provides a scalability factor of 5 in a motion-compensated subband video coding scheme. Depending on the sequence characteristics, motion model etc., this structure can provide high coding performance. Also, it gives a better coding efficiency for the temporal approximation subbands, thus leading to an improved temporal scalability. It addresses video surveillance applications, where the motion is very low in most cases.

In this paper, we consider the problem of real-time multimedia transmission among several peers (users). The peers use a heterogeneous wireless multi-hop mesh network for the delivery of these high-bandwidth streams. One of the main challenges of the considered problem is the division of the scarce wireless resources among the various peers. To address this problem, we propose an efficient, distributed and collaborative framework for wireless resource exchanges that enables peers to divide available wireless resources among themselves based on their quality of service (QoS) requirements, the underlying channel conditions and network topology. The scalable coding of the video content and decomposition of video flows into various sub-flows (priorities) allow peers to transfer the video at different quality levels, depending on the network load. Users collaboratively decide which of their sub-flows to admit, and which paths these sub-flows should be transmitted on in order to maximize a system defined utility. Our results show that with user collaboration, these distributed algorithms provide system and user performance comparable to a centralized exhaustive implementation.