The encoder usually uses the motion estimation and compensation (ME/MC) method to utilize the correlations between the frames that require high computation. Thus, the conventional schemes are not apt for low power real-time applications, i.e., VSN (battery-powered nodes with primitive hardware). Most of the conventional schemes entail the use of extra computational resources for data processing leading to additional power requirements, with few trade-offs between the reconstruction quality and computational constraints [1]. The emergence of compressive sensing (CS) [3] has widened the approach for 3D reconstruction, multi-camera imaging, armed personnel tracing and surveillance, magnetic resonance imaging (MRI) and seismic identification for applications such as surveillance, mental state monitoring, driver drowsiness and patient care, etc. CS is specifically useful for the transmission of correlated data for low power real-time applications, i.e., visual sensors networks and wireless sensors networks. CS has transformed the idea of conventional data compression schemes as it is based on the principle of exemplifying a signal using a much smaller sampling rate than the standard Nyquist rate. Hence, using CS is one of the best solutions. Unlike the conventional schemes it is based on a simple encoder (i.e., transmitting fewer data) and a complex decoder. It is also known that the primary motivation for considering CS is that it offers the potential to reduce sensor costs and computational complexity significantly. For example, it is proposed that the famous single-pixel camera, with only one photo-sensor, could provide an imaging sensor that is much cheaper to build than one with several million photosensors. Moreover, CS is a form of dimensionality reduction only, and it is not specifically a type of compression [4]. CS makes sense only when the dimensionality reduction that it provides takes place directly within the sensing device's hardware. In other words, the image never exists anywhere in the sensor in its full dimensionality, unlike most of the conventional video coding schemes in which the acquired image is used in its full dimensionality, increasing the load on the sensor node. However, CS can be used for compression, provided that the dimensionality reduction it offers is coupled with quantization and entropy coding to create a bit of data from the CS measurements. In this paper, a joint decoding (JD) framework is proposed. The proposed scheme aims to decrease the redundancies among video frames at the decoder using side information. The proposed scheme first decodes the encoded keyframes and non-key frames of a video sequence received at the host workstation. After independently decoding the frames, a side information process is initiated. The side information produced is utilized to enhance the reconstruction quality of the non-key frames. The side information process consists of registration and fusion steps used to exploit the inter/intra view correlation between the frames to generate a frame prediction. Next, the difference among the predicted frame measurements and the acquired measurements is calculated to minimize the prediction errors. The difference at the level of the measurement, known as the residual measurements, is recovered to produce the residual. The final frame reconstruction is performed by adding the residual and the predicted frame to compensate for the difference. The rest of the paper is organized as follows: Section 2 presents an overview of the various existing CS-based video reconstruction coding schemes. A detailed explanation of the proposed JD scheme for single view video reconstruction using block CS is provided in Section 3. In Section 4 the experimental results are presented and the conclusions are drawn in Section 5.