## 1 Introduction  
Wireless sensor networks (WSNs) composed of tiny low-power sensors that cooperatively operate in an _ad hoc_ manner have fostered a series of innovative monitoring and control applications. Harvesting scalar information from deployed sensors in a monitored field such as humidity, pressure, temperature, luminosity, seismic variations, among others, these applications have raised many challenges that have been extensively addressed by the academic community [1-3]. Nodes in WSNs are disposable electronic devices commonly equipped with a transceiver, a limited energy supply, a sensing unity and memory and processing resources, although additional modules can be found, such as a Global Positioning System (GPS) [3]. When inexpensive low-resolution CMOS cameras and/or microphones are embedded in wireless sensors, multimedia and scalar data can be retrieved from the environment, allowing a new range of applications. The resulting Wireless Multimedia Sensor Networks (WMSNs) enhance our understanding of the physical world, improving applications like surveillance, disaster monitoring, wildlife observation, automated assistance for elderly and disabled people, traffic avoidance, industrial process control and localization services, just to cite a few [4-6]. In such contexts, when multimedia data is collected exclusively by video-based sensors, the resulting networks are referred to as Visual Sensor Networks [7]. In sensor networks comprised of visual sensors, video and/or images are collected from the environment in a different way from traditional WSNs. Most cameras installed at source nodes are not omnidirectional, resulting in a directional sensing capability usually referred as the cameras' Field of View (FoV) [8]. In fact, the FoV is a sector-like visible region emanating from the camera, defining a direction of viewing (the camera's pose). Viewing angle, lens quality and zoom capabilities, as well as the type of the camera used in source nodes (fixed or PTZ [8]), are major factors that influence the resulting FoV and the way visual data are retrieved from the environment. The visual data sensed by source nodes have to be digitalized and transmitted to the sink over the sensor network. The energy and processing constraints of the sensor nodes, as well as the nature of the wireless links that interconnect them, restrict the attainable bandwidth of the communication path(s) and impose a considerable packet loss rate. Among the adopted solutions, multimedia coding techniques are used to compress the original data, reducing the required transmission rate and potentially saving energy of the source node and over the entire path(s) toward the sink. Additionally, some multimedia coding provides error resilience, which may sustain the minimum acceptable end-to-end quality of the application, even when some packets are lost while transmitted over error-prone wireless links. The available multimedia coding techniques differ in the way they process data, directly impacting the final transmission rate, the expected end-to-end quality and the coding complexity in terms of energy consumption and required processing time and computational resources. Moreover, the natureof the target application also influences the multimedia coding, since the type of monitoring to be performed will affect the expected quality of the received media. In visual sensor networks, it is crucial to optimize communication in order to minimize energy consumption and to maintain an acceptable quality of the application. The academic community has addressed such issues proposing multimedia-based cross-layer optimizations. In short, protocols following cross-layer design disrupt the concept of modularized layers, which reduce the overhead and optimize the jointly design of network protocols in order to meet the specific requirements of VSNs [3,6].