In recent years, the delivery of video streaming services in Internet is popular and full of potential. However, an equal error protection scheme to all video packets in Internet will significantly degrade the video quality since the encoded and packetizated video packets have different significances.

    Therefore, this thesis proposes a Significance Classification mechanism (SC-TS) to classify the video packet importance from Temporal and Spatial domain simultaneously. SC-TS not only determines packet significance from the frame order in time domain but also utilizes error tracking concept to differentiate significances of video packets in the same frame. Moreover, for satisfying various video sequences with different coding properties, this thesis adds a learning algorithm of error propagation property to SC-TS, which is named Adaptive SC-TS (ASC-TS) in this thesis. While utilizing ASC-TS, the required error propagation ratio of next GOP is learned from the error propagation results of current GOP.

    Simulation results reveal that the proposed ASC-TS mechanism can effectively improve the received picture quality up to 0.7dB under the same network environment, compared with the traditional classification mechanism that determines the packet signification from temporal domain only.