Abstract: We propose a Rate-Distortion (RD) optimized strategy for frame-dropping and scheduling of multi-user conversational and streaming videos. We consider a scenario where conversational and streaming videos share the forwarding resources at a network node. Two buffers are setup on the node to temporarily store the packets for these two types of video applications. For streaming video, a big buffer is used as the associated delay constraint of the application is moderate and a very small buffer is used for conversational video to ensure that the forwarding delay of every packet is limited. A scheduler is located behind these two buffers that dynamically assigns transmission slots on the outgoing link to the two buffers. Rate-distortion side information is used to perform RD-optimized frame dropping in case of node overload. Sharing the data rate on the outgoing link between the conversational and the streaming videos is done either based on the fullness of the two associated buffers or on the mean incoming rates of the respective videos. Simulation results showed that our proposed RD-optimized frame dropping and scheduling approach provides significant improvements in performance over the popular priority-based random dropping (PRD) technique.

Key words: Rate-distortion optimization, Video frame dropping, Conversational video, Streaming video, Distortion matrix, Hint tracks, Scheduling, Resource assignment

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