The problems of efficient codec and resiliency to channel errors are important in video processing of wireless multimedia sensor networks (WMSNs). Based on the compressed sensing (CS) and dictionary learning algorithm, a dictionary learning-based compressed video sensing codec model is proposed for the WMSNs. The model uses CS to reduce the complexity of encoder effectively and improve resiliency to channel errors. In the encoder, the application of difference structure and skip mode reduces the amount of data transmitted in the channel. And in the decoder, dictionary learning algorithm helps enhance images′ sparse representation, thereby improve reconstructed video quality. The model switches the computational complexity from the encoder to the decoder and has high coding efficiency, so it can be applied to the recource-constrained embedded devices. The theory analysis and experiment results have verified the feasibility and efficiency of the model.