We present a hardware-friendly spatiotemporal compressed sensing framework for video compression. The spatiotemporal compressed sensing incorporates random sampling in both spatial and temporal domain to encode the video scene into a single coded image. During decoding, the video is reconstructed using dictionary learning and sparse recovery. The evaluation results demonstrate the proposed approach can achieve high compression rate (10 : 1-30 : 1) and robustness reconstruction quality (> 20dB) on noisy database. Additionally, it also enables power efficient and real-time CMOS implementation (0.7 nJ/pixel).