Down-sampling followed by an up-sampling is a well-known strategy to compress high-resolution pictures given a limited bandwidth in image as well as video coding. Recently, inspired by the latest advances of image super resolution (SR) technologies using convolutional neural network (CNN), CNN-based SR has been explored for resampling-based coding. However, the side information generated during the compression process is not utilized efficiently by the SR network in prior arts. In this paper, we propose a CNN-based SR method for video coding, where more side information is leveraged as a supplement to reconstruction samples. Specifically, we introduce prediction samples to be the auxiliary information, as it can provide the texture and directional information about the original picture. Considering the different characteristics, we design different networks for the luma and chroma components. When designing the chroma up-sampling CNN, the luma reconstruction is used as the auxiliary information of the chroma network, which exploits the cross-component correlation. Experimental results show that the proposed method achieves 11.07% BD-rate savings in all-intra configuration compared with VTM-11.0. Further experiments validate the effectiveness of using luma information to aid the chroma up-sampling process.