## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Video-Based Cross-Layer Optimization
#### 4.2.1 Predictive Video Coding  
Most research on video-based cross-layer optimization in the last few years has considered predictive encoding as the main option for video monitoring applications in resource-constrained wireless sensor networks. Among the possible reasons for such a tendency, predictive codecs have been widely used in the Internet, with many possibilities in compression levels and expected quality. Moreover, we have noted that the inner characteristics of such codecs are very propitious for cross-layer optimization, resulting in higher efficiency in visual sensor networks. The work in [38] regards multipath predictive-encoded video transmission in wireless sensor networks, proposing two packet scheduling algorithms to be used when the aggregated bandwidth of the paths is lower than the transmission requirements of the applications. The idea is to discard less relevant frames in order to decrease the source transmission rate, adapting to the current resources of the sensor network. The authors define a recursive distortion prediction model to identify the expected relevance of the frames. The proposed prediction model regards different types of bit errors during transmission (isolated, burst of losses and errors separated with a small lag) [39, 40], since the nature of the error can influence the quality of the received media. Based on this model, the proposed algorithms know what frames could be discarded prior the transmission, without increasing significantly the video distortion in the receiving side. In that proposal, the images collected by sensor nodes are encoded using the H.264 codec, due to its high compression efficiency. This codec uses previously encoded frames as reference for motion-compensated prediction. In experiments, the authors consider a frame rate of 30 fps with QCIF resolution (176 \(\times\)144), and encoded data being transmitted in RTP (Real-time Transport Protocol) [41] packets of 1,024 bytes size. The proposed algorithms in [38] operate on the cluster-based routing protocol LEACH (Low-Energy Adaptive Clustering Hierarchy) [42], which was adapted to support multipath routing and improve the energy consumption on cluster heads. Despite that, the original data is only processedby the source node, in a different way from [33-35]. The proposed recursive distortion prediction model is very promising for visual sensor networks. That model considers a robust error theory which can be useful for real-world implementations. Some drawbacks rely on the resulting complexity for constrained-resources sensors and the use of UDP/IP mechanisms. Internet-based transport and network protocols are unsuitable for most wireless multimedia sensor networks, as already stated by the academic community [5,6]. Following this same investigation line, the work in [43] proposes a video compression logical sub-layer which defines a new compression model able to prioritize frames. Moreover, the authors define protocols for the transport and network layers. The proposed solution is defined as the Energy-efficient and high-Quality Video transmission architecture (EQU-Architecture). In short, the EQV-Architecture extends the lifetime of the network assuring the sufficient level of the video quality. The proposed compression model defines the M-MPEG codec (Modified-MPEG) based on MPEG-2. Compression is accomplished through M-frames and D-frames, all created using an extension of JPEG to prioritize image blocks. The main idea of this work is to create more relevant encoded frames that should be transmitted through highly reliable schemes, while flows of less relevant frames use semi-reliable schemes. Among the proposed solutions, more relevant frames are subject to Reed-Solomon forward correction codes. The type of the employed transmission service (reliable or semi-reliable) is structured over two packet dropping strategies: energy aware dropping and random early dropping.