## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Video-Based Cross-Layer Optimization
#### 4.2.3 Distributed Video Coding  
Predictive coding is dominated by motion estimation, with the complexity relying on the encoder side. According to [PERSON] _et al._[61], predictive video encoders may be indeed 5-10 times more complex than decoders. For visual sensor networks, such complexity may demand more computational power of source nodes for the encoding process, also resulting in additional energy consumption. A feasible solution to shift the complexity to the decoder side is to employ DVC as the coding technique. Distributed video coding is a video compression paradigm based on [PERSON]'s [15] and [PERSON]'s [16] theoretical results. The idea is to transmit intra-coded frame along with side information frame. The intra-coded frames are generated using some technique for image processing, as DCT or pixel-based compression [62]. The side information frames are intended to be processed at the decoder, which exploit the temporal correlation among the frames. As motion information is only considered at the decoder side, DVC demands less computational power and energy at the encoder side than predictive video coding, also providing more error-resilience [63]. Most works in the literature that consider DVC as the coding technique are concerned with quality distortion due to bit errors and packet dropping. In this way, [PERSON] _et al._[64] defined a communication environment comprised of a main camera sensor with high processing capabilities and many deployed wireless sensors to cover different views of the target scene. The idea is to code the visual data from the main camera using usual intra-frame coding techniques, encoding the remaining data from the other sensors with DVC. The DVC scheme Pixel-Domain Wyner-Ziv (PDWZ) is employed along with an adaptive rate control mechanism intended to control the number of parity bits based on feedback from the decoder side. The transmission adjustment in that work is based on the application layer, subject to feedback messages transmitted from the decoder side. [PERSON] _et al._[65] define an unequal error protection scheme to assign different protection levels to the different elements of a DVC compressed video. In the feedback aided unequal error protection scheme, a feedback channel provides information that are used to adjustment the parity data rate. A second scheme adjusts the correction data according to the amount of motion information being transmitted by the source. The work presented in [66] proposes a rate control method that considers the channel loss and the correlation of video images, providing more resilience to packet errors when transmitting DVC encoded video. The rate adjustment is performed controlling the quantization parameters of the DVC encoding, adjusting the number of parity bits for error-resilience. Doing so, the expected overall quality for the proposed method is higher than the conventional method (adjustment based only in correlation of video images) when transmitting packets over error-prone links. Both works presented in [65] and [66] adjust the amount of parity data according to the current state of the network and/or the relevance of the transmitted coded data. Despite the fact that their resultscontribute to error recovery in VSNs where DVC is employed, neither of them conducts sufficient analysis on the energy consumption in source nodes throughout the active paths. Distributed video coding is being constantly proposed as the most suitable coding option for wireless multimedia sensor networks, but we are not convinced of that. It is a fact that DVC achieves energy saving in source nodes, shifting the complexity to the receiver end, but most energy consumption is expected to occur in the communication process, and predictive coding achieves high compression. Other relevant consideration is that for many applications, monitoring is not expected to be performed until the complete energy consumption of the nodes.