This paper proposes a compressive sensing of video (CSV) framework that utilizes statistical properties of video signal. The proposed scheme periodically acquires deterministic measurements (i.e., low frequency DCT coefficients) for key frames to improve recovering both key and nonkey frames. In addition, based on temporal correlation among frames, side information is generated for nonkey frames to model their important coefficients and sparsity in transform domain. This information helps better sensing by weighted sensing and measurement allocation. Experimental results show effectiveness of the proposed techniques and their significant improvement compared to prior work.