The developments of science and technology have brought rapid growth of data, of which video and image data account for a high percentage. How to efficiently handle these data and find valuable information from them is a hot topic. Big data are characterized by four Vs: volume, velocity, variety, and value, representing large amounts of data, quick data processing, various data types, and low value density, respectively. Video big data share all these characteristics, and often come with much greater data redundancy than other types of data. As a result, they call for more efficient techniques for compression and processing. The research of video big data is primarily carried out along four dimensions: video data representation, intelligent video analysis, video compression and transmission, and video display and quality evaluation. Recent trends show that video representation is becoming more realistic and intelligent, and video analysis more accurate in identification and classification thanks to the deep neural networks. At the same time, video compression promises to be more efficient with new methods to reduce coding complexity, and less redundant with the help of visual perception aware coding algorithms. In accordance with more advanced video representation, video display devices are undergoing hardware upgrades, guided by a comprehensive methodology of video quality evaluation that is centered around quality of experience, instead of the traditional criteria developed for image quality assessment.