Compressive video sensing (CVS) has great research significance in the video acquisition system with limited sampling resources. In this paper, we proposed a reconstruction algorithm based on total variation (TV) and nonlocal low-rank regularization (NLR-CS) to better reconstruct video signal from compressive sampled data. This algorithm consists two steps: The first step considers local correlation between and within video frames, applies TV as the prior constraint to obtain the initial recovered frame; In the second step, the improved NLR-CS algorithm is utilized to further reconstruct video frame considering the nonlocal self-similarity (NLSS). This step first blocks the initial recovered frame, finds similar blocks in the current frame and the key frames to construct low-rank matrix, then a low-ranking regularization reconstruction is performed. Experimental results show that the proposed algorithm can reconstruct video signals well, obtains higher video reconstruction accuracy than other CVS reconstruction algorithms.