Information-Constrained Resource Allocation in Multicamera Wireless Surveillance Networks

Real-time multiuser multimedia applications, such as surveillance or monitoring using multiple cameras, have recently started to be deployed over flexible and low-cost multihop wireless networks. In such multimedia systems, the various sources (cameras) share the limited network resources and collaboratively forward the captured video streams to a remote central monitor. However, existing resource allocation schemes often ignore the dynamic application-layer video and network characteristics by focusing on the steady-state or worst-case operating conditions. This may result in inefficient allocation of the network resources. In this paper, we focus on determining whether the resource allocation for wireless video surveillance systems should be performed based on steady-state or worst-case operating conditions, or whether perpetual adaptation to the dynamically changing source and network conditions is desirable. We analyze three different types of solutions that have different information requirements: a centralized optimization approach, a decentralized game-theoretic approach (which guarantees a stable allocation), and a distributed greedy approach (which perpetually adapts allocation based on the local information exchanged among the neighboring nodes). We compare these three approaches using the following four metrics: 1) the total video quality; 2) the computational complexity; 3) the required control information overhead; and 4) the timely adaptation to the network and source variation. We show that in a static network, the game theoretic resource allocation is only better than the distributed greedy approach when the network transmission rates are high. In a dynamic network, the distributed greedy approach can outperform the other two approaches significantly in terms of video quality (peak signal-to-noise ratio). This shows that resource allocation solutions for multicamera wireless surveillance networks need to explicitly consider both the dynamic source characteristics and network conditions, rather than always relying on stable, but predetermined, allocations.