## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Image-Based Cross-Layer Optimization
#### 4.1.2 Wavelet-Based Image Coding
Using information about the end-to-end distortion constraints and the estimated channel condition, the proposed solution in [19] can adaptively determine the number of encoding layers to be transmitted. In other words, it is possible to adjust the source coding rate, the source level error resilience scheme and the transmitter power level. What the proposed solution does is to find the optimal number of encoding layers to be transmitted and the optimal strategies for each layer. Although designed to be used in generic wireless sensor networks, in [19] only the case where the transmitter can directly communicate with the receiver is considered. This is the reason why the proposed solution defines adjustment of the transmitter power according to the current network condition. For many applications, sensors are expected to be randomly deployed, since it is easier and less expensive for large wireless sensor networks [8]. Moreover, it is common to expect dense deployment with a huge number of nodes. In such case, direct communication between source node and the sink is not practical, demanding excessive energy consumption and increasing the sensor costs in most of VSN applications. The work presented in [29] exploits the overlapped area created by the field of view of source image sensors, when there are inter-sensor correlations [8]. Instead of saving energy by employing in-network compression algorithms, the authors propose that the source sensors transmit only visual data corresponding to areas with no overlapping and additionally some part of the covered overlapped area (avoiding transmission of visual information that has been already transmitted by other sources). Thus, the total transmitted data is reduced throughout the network, and the end-to-end quality of the application is preserved, since the FoV of at least two sensors overlaps in the desired communication scenario. In such case, extra processing is expected from the sink, which has to recreate the viewed images based on the received packets. In [29] the Multi-Level Rate-Oriented Routing (MLRR) is proposed, a routing scheme where lower transmission rates should be associated with the nodes having less residual power energy, potentially prolonging the network lifetime. For that, the considered paths are node-disjoint and should respect the rate constraints of the sources. The concept of using overlapping areas to reduce energy consumption due to lower transmission rates is very promising, but presents some drawbacks. As deployed video-based sensors may have a unique view of a target or scene, even overlapping sensors may produce different visual information [8] (for example, viewing the front and back sides of a person). On the other hand, if two or more source sensors have exactly the same view, only one has to be active at a given time. So, for some types of visual monitoring, the proposed solution may have restricted applications. The work presented in [30] proposes a position based cross layer resource allocation approach to achieve optimal image transmission quality while assuring energy efficiency in wireless multimedia sensor networks. For that, the unequal importance among image-pixel-position information (p-data) and image-pixel-value information (v-data) is exploited, following the wavelet image compression paradigm. As p-data are more important, they receive more protection by UEP, while the relatively unimportant v-data segments are less protected, improving energy efficiency. The loss probability reduction of p-data increases the attainable perceptual quality of the transmitted image, while the loss of v-data segments has low impact. So, the increased energy consumption for more protection on important p-data segments is compensated by the less protection on unimportant v-data segments. The level of protection on each type of coded data is subject to the energy budget on each link, which is assumed to be provided by network layer.