@article{CFS+08,
title = { Symbolic quality control for multimedia applications },
author = {Combaz, Jacques and Fernandez, Jean-Claude and Sifakis, Joseph and Strus, Lo\"ic},
month = {OCT},
year = {2008},
journal = {Real-Time Systems},
number = {1},
pages = {1-43},
publisher = {Springer},
volume = {40},
team = {DCS, RSD},
abstract = {We present a fine grain quality control method for multimedia applications. The method takes as input an application software composed of actions. The execution times of actions are unknown increasing functions of quality level parameters. The method allows the construction of a Controller which computes adequate action schedules and corresponding quality levels, so as to meet QoS requirements for a given platform. These include requirements for safety (action deadlines are met) as well optimality (maximization and smoothness of quality levels). The Controller consists of a Quality Manager and a Scheduler. For each action, the Controller uses a quality management policy for choosing a schedule and quality levels meeting the QoS requirements. The schedule is selected amongst a set of optimal schedules computed by the Scheduler. We extend and improve results of previous papers providing a solid theoretical basis for designing and implementing the Controller. We propose a symbolic quality management method using speed diagrams, a representation of the controlled systems dynamics. Instead of numerically computing a quality level for each action, the Quality Manager changes action quality levels based on the knowledge of constraints characterizing control relaxation regions. These are sets of states in which quality management for a given number of computation steps can be relaxed without degrading quality. We study techniques for efficient computation of optimal schedules. We present experimental results including the implementation of the method and benchmarks for an MPEG4 video encoder. The benchmarks show drastic performance improvement for controlled quality with respect to constant quality. They also show that symbolic quality management allows significant reduction of the overhead with respect to numeric quality management. Finally, using optimal schedules can lead to considerable performance gains.},
}
