## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Image-Based Cross-Layer Optimization
#### 4.1.1 Progressive Image Coding  
[PERSON] _et al._[20] proposed the Reliable Synchronous Transport Protocol (RSTP) for synchronization of image transmission from multiple sources, assuring the same level of quality for the received images and a fairer utilization of the available bandwidth. This is accomplished by employing the JPEG codec in progressive encoding mode, where the source images are compressed through multiple scans with progressively increasing details. Using such coding technique, a low-quality version of the image can be exhibited very quickly, and gradual quality refinements follow. In [20], one practical effect of progressive encoding is that for low-bandwidth and error-prone environments, the images may be displayed entirely, but with low quality. This coarse-to-clear presentation mode may be more desirable than the slow top-to-bottom mode of baseline JPEG. Employing JPEG in progressive mode, RSTP can show smoothly the images with increasing precision. To perform the desired synchronization, the authors define both frame level and quality level synchronization. The frame level synchronization is concerned with the number of frames transmitted per second (fps). On the other hand, quality level synchronization adjusts parameters of the source encoding, also impacting end-to-end quality and required transmission rate. RTSP is a TCP-based transport protocol that can synchronize the source nodes to achieve equivalent levels of quality, potentially adapting to the current state of the network (available bandwidth, packet loss rate and congestion, _etc._). The main concept behind RSTP is the transmission management at the receiver end, where the sink controls the transmissions from the source nodes. Additionally, congestion and error control are specified in this transport protocol. The RTSP transmission management is further investigated in [21]. As a promising contribution, the authors propose a mosaic algorithm to align multiple images, achieving a larger field of view. An interesting consideration about [20] and [21] is that both works expect that JPEG will be the dominant technique for image sensor nodes. However, the energy consumption and computational complexity of the employed codec are not investigated, nor are other possibilities considered. [PERSON] and [PERSON] [22] also investigated the JPEG codec in low-power and low-bit-rate wireless networks, exploiting progressive JPEG encoding as in [20]. In that work a coding strategy that only employs high quality encoding to parts of the image with higher relevance for the application is proposed, leaving the remaining data with huge compression (and lower quality). At the destination, all parts of the same image (potentially having some prioritized parts) are reassembled, resulting in different qualities in the same image. The amount of data encoded in one scan is equal to or less than the bandwidth of the network. Such approach is slightly different from the work in [20], which varies the quality of entire atomic images. The image-based cross-layer optimization proposed in [22] is a priority-driven scheduling algorithm. That algorithm schedules for transmission more data from important sub-images, according to the available bandwidth. Additionally, when it is impossible to transmit the entire image within the current bandwidth and power constraints, some data pertaining to unimportant sub-images are discarded. VSN applications can benefit for visual monitoring where priority-based JPEG encoding like the one presented in [22] is used in source nodes. For example, if applications are monitoring a moving target, such a coding strategy would potentially reduce the bandwidth requirements (and energy consumption) of transmission of less-relevant parts of the scene (as visual data of the sky and the ground), keeping the viewing of the target with high quality for the application. Progressive image encoding is also exploited in [23].