The bitrate of a video bitstream directly determines whether it can be transmitted successfully under current network circumstances. Under extreme circumstances, environments may require the video to be transmitted at a bitrate lower than normal application scenarios. Encoding videos with a rescaling strategy is a possible way to solve this problem. In this paper, we developed a content-adaptive variable resolution framework for intra coding to solve the bitrate adaptation problem. Specifically, we proposed a double-line hyperbolic R-D model to characterize the relationship between bit per pixel (bpp) and mean square error (MSE) in wide bitrate range at different resolutions. Then, we employed convolutional neural network (CNN) to predict model parameters of different coding resolution from video content. Finally, we determined the coding resolution under current bandwidth condition. Without modifying the encoder or decoder, experiments on multiple videos with different contents show that our proposed framework can predict the encoding resolution with high accuracy. In addition, experimental results show that our proposed framework for variable resolution coding outperforms fixed down-sample ratio method in x265 intra coding, leading to on average 4.67% BD-rate reduction.