Semantic Based Error Avoidance and Correction for Video Streaming
Video streaming over best effort networks remains a challenging task. Video quality decreases with an increasing number of frames that are corrupted, lost or only received after playback time. We use semantic information about the video and the network to decide between alternative or cooperative streaming sources to avoid or to correct data loss. We propose a distributed architecture that combines a peer-to-peer indexing archive for videos with error avoidance and error correction mechanisms to select the best delivery method from the corresponding sources. Our indexing-cache peer-to-peer overlay has two interesting properties for our selection model: it efficiently locates several sources for a video (if they exist) and even rare videos. Based on the coding characteristics of the available videos and the state of the network we apply a model for selecting between error avoidance, error correction and a combination of both approaches. This model is evaluated by using the network simulator NS-2 and a modified version of EvalVid.
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