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A queuing-theoretic approach to task scheduling and processor selection for video-decoding applications

IEEE TRANSACTIONS ON MULTIMEDIA 2007年第7期9卷 1493-1507页

作者： Mastronarde, Nicholas H. van der Schaar, Mihaela Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

We propose a cross-layer design for resource-constrained systems that simultaneously decode multiple video streams on multiple parallel processors, cores, or processing elements. Our proposed design explicitly considers the coder specific application characteristics such as the decoding dependencies, decoding deadlines, and distortion impacts of different video packets (e.g., frames, slices, groups of slices etc.). The key to the cross-layer design is the resource management control plane (RMCP) that coordinates the scheduling and processor selection across the active applications. The RMCP deploys a priority-queuing model that can evaluate the system congestion and predict the total expected video quality for the set of active decoding tasks. Using this model, we develop a robust distortion- and delay-aware scheduling algorithm for video packets. This algorithm aims to maximize the sum of achieved video qualities over all of the decoded video sequences. Additionally, we propose a processor selection scheme intended to minimize the delays experienced by the queued video packets. In this way, the number of missed decoding deadlines is reduced and the overall decoded video quality is increased. We compare queuing-theoretic based scheduling strategies to media agnostic scheduling strategies (i.e., earliest-deadline-first scheduling) that do not jointly consider the decoding deadlines and distortion impacts. Our results illustrate that by directly considering the video application's properties in the design of a video decoding system, significant system performance gains on the order of 4 dB peak-signal-to-noise ratio can be achieved.

关键词： middleware multiprocessor decoding multimedia systems queuing models for systems real-time system scheduling video decoding complexity

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