## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Image-Based Cross-Layer Optimization
#### 4.1.2 Wavelet-Based Image Coding  
Wavelet-based image compression is also exploited for cross-layer optimization. The work [24] proposes an energy-efficient self-adaptive image transmission scheme, providing a trade-off between the energy consumption to transmit encoded images and the quality of the received media at the sink. The proposed scheme is based on DWT and different levels of reliable transmission. In fact, the DWT technique defines priorities for the encoded data, since the image can be decomposed into separate subbands. Typically, wavelet-based image compression involves two-dimensional wavelet decomposition of the original image, giving low and high frequency subbands. Wavelet coefficients are quantized, coded and transmitted as a bit stream. Whatever the chosen prioritization scheme in [24], full reliable transmission is only required for high-priority data, reducing the energy consumption throughout the path toward the sink. The remaining data is transmitted in a semi reliable mode. The reliability mechanism proposed in [24] is based on the way intermediate nodes treat packets before they are relayed. When the energy resource of intermediate nodes is lower than a given threshold, low-priority packets are discarded while high-priority packets are relayed through the current path toward the sink. The expected outcome is the prolonging of the network lifetime, keeping an acceptable quality level for the received images. In order to avoid discarding packets that have crossed many hops, resulting in an undesired waste of energy, the authors of [24] consider the energy consumption in preceding nodes. Such a consideration is further investigated in [25], which defines two general packet dropping schemes for the work in [24]. In the open-loop scheme, it is only considered the energy of the intermediate node. On the other hand, close-loop also regards the available energy in the next intermediate nodes to the sink, which could help in prediction of the dropping probability of the transmitted packets. [PERSON] and [PERSON] [26] propose a computational solution to mitigate congestion by reduction on the data transmission rate with low sacrifice of the end-to-end application quality. That work defines the Adaptive Compression-based congestion control Technique (ACT) aimed at the control of congestion by a reduction in the number of transmitted packets, but keeping the received data quality. For that, ACT employs the following compression techniques: DWT, Adaptive Differential Pulse Code Modulation (ADPCM) and Run Length Coding (RLC). In fact, that work does not directly define a codec for multimedia compression, as in [19, 24], but the compression algorithms used in [26] may be used for image coding, since DWT and RLC are used by some codecs of the JPEG family. As mentioned before, the DWT technique indirectly defines priorities for the encoded data. In case of congestion, such priorities are considered by intermediate nodes. The idea is to discard packets containing less relevant data when the congested node may chose what packets must to be discarded. Additionally, ADPCM reduces the amount of transmitted data from the source using the principle of quantization, while RLC generates a smaller number of packets for low-priority data. Putting these all together, less data must be transmitted, saving energy throughout the active congested path toward the sink with low degradation in the end-to-end perceptual quality. The work in [19] aims at adaptive reduction of energy consumption in processing and transmission of images in wireless sensor networks. Energy saving is achieved through image compression using JPEG2000 codec [27, 28], according to the acceptable quality of the transmitted images. JPEG2000 codec is a wavelet-based still image compression standard that presents high compression performance and strong error resilience.