We consider the problem of optimally configuring classifier chains for real-time multimedia stream mining systems.
Jointly maximizing the performance over several classifiers under minimal end-to-end processing delay is a difficult task
due to the distributed nature of analytics (e.g. utilized models or stored data sets), where changing the filtering process at
a single classifier can have an unpredictable effect on both the feature values of data arriving at classifiers further
downstream, as well as the end-to-end processing delay. While the utility function can not be accurately modeled, in this
paper we propose a randomized distributed algorithm that guarantees almost sure convergence to the optimal solution.
We also provide results using speech data showing that the algorithm can perform well under highly dynamic
In this paper we propose a joint resource allocation and scheduling algorithm for video decoding on a resource-constrained
system. By decomposing a multimedia task into decoding jobs using quality-driven priority classes, we
demonstrate using queuing theoretic analysis that significant power savings can be achieved under small video
quality degradation without requiring the encoder to adapt its transmitted bitstream. Based on this scheduling
algorithm, we propose an algorithm for maximizing the sum of video qualities in a multiple task environment, while
minimizing system energy consumption, without requiring tasks to reveal information about their performances to
the system or to other potentially exploitative applications. Importantly, we offer a method to optimize the
performance of multiple video decoding tasks on an energy-constrained system, while protecting private
information about the system and the applications.