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In the last decade, wireless multimedia device is widely used in many fields, which leads to efficiency improvement, reliability, security, and economic benefits in our daily life. However, with the rapid development of new technologies, the wireless multimedia data transmission security is confronted with a series of new threats and challenges. In physical layer, Radio Frequency Fingerprinting (RFF) is a unique characteristic of IoT devices themselves, which can difficultly be tampered. The wireless multimedia device identification via Radio Frequency Fingerprinting (RFF) extracted from radio signals is a physical-layer method for data transmission security. Just as people’s unique fingerprinting, different Internet of Things (IoT) devices exhibit different RFF which can be used for identification and authentication. In this paper, a wireless multimedia device identification system based on Ensemble Learning is proposed. The key technologies such as signal detection, RFF extraction, and classification model are discussed. According to the theoretical modeling and experiment validation, the reliability and the differentiability of the RFFs are evaluated and the classification results are shown under the real wireless multimedia device environments.