Block-based compressive imaging enables new video acquisition methodology while reducing raw data size, theoretically eliminating the need for complex coding algorithms. When transmitting raw data, however, the redundancy associated with random projection remains. This paper takes a fresh look at raw data structure by viewing it as cube made up of multiple downsampled images rather than a vector. As a result, we can view each individual data point as a pixel, allowing us to code more flexibly and versatility than state-of-the-art works. Following that, we propose a tailored video coding framework for this structure that includes directional 9 modes intra and inter prediction with block-matching motion estimation, transformation, and quantization. We evaluated coding performance using various 4K datasets, resulting in 60-65% lower bit-per-pixels while maintaining visual quality compared to state-of-the-art works.