Video Question Answering (Video QA) aims to give an answer to the question through semantic reasoning between visual and linguistic information. Recently, handling large amounts of multi-modal video and language information of a video is considered important in the industry. However, the current video QA models use deep features, suffered from significant computational complexity and insufficient representation capability both in training and testing. Existing features are extracted using pretrained networks after all the frames are decoded, which is not always suitable for video QA tasks. In this paper, we develop a novel deep neural network to provide video QA features obtained from coded video bit-stream to reduce the complexity. The proposed network includes several dedicated deep modules to both the video QA and the video compression system, which is the first attempt at the video QA task. The proposed network is predominantly model-agnostic. It is integrated into the state-of-the-art networks for improved performance without any computationally expensive motion-related deep models. The experimental results demonstrate that the proposed network outperforms the previous studies at lower complexity. https://github.com/Nayoung-Kim-ICP/VQAC.