Although learned image compression methods have achieved competitive rate-distortion performances, learned video compression remains challenging. The current mainstream learned video compression frameworks usually improve the motion prediction module to reduce the redundancy in video sequences. Although these methods can achieve a great compression ratio, they often ignore the improvement in the entropy model, which does not fully exploit the temporal and spatial characteristics in the video. Meanwhile, these methods always suffer from the error propagation problem. To solve those problems, we propose a learned video compression framework with channel-wise autoregressive entropy model. Our framework captures spatial–temporal dependencies through a powerful entropy model to reduce the redundancy in video sequences. In particular, we do not directly compress the frames in the pixel domain, with no need for the motion prediction module, avoiding the error propagation problem. To better utilize the temporal contexts of the previous frame, we propose the window temporal prior module. Experiments show that our proposed video compression framework achieves promising compression effects in terms of peak signal-to-noise ratio and multiscale structural similarity.