In this paper, we propose a new resource allocation framework for multimedia systems that perform multiple simultaneous videodecoding tasks. We jointly consider the available system resources (e.g., processor cycles)...
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In this paper, we propose a new resource allocation framework for multimedia systems that perform multiple simultaneous videodecoding tasks. We jointly consider the available system resources (e.g., processor cycles) and the videodecoding task's characteristics such as the sequence's content, the bit-rate, and the group of pictures (GOP) structure, in order to determine a fair and optimal resource allocation. To this end, we derive a quality-complexity model that determines the quality [in terms of peak signal-to-noise ratio (PSNR)] that a task can achieve given a certain system resource allocation. We use these quality-complexity models to determine a quality-fair and Pareto-optimal resource allocation using the Kalai-Smorodinski Bargaining Solution (KSBS) from axiomatic bargaining theory. The KSBS explicitly considers the resulting multimedia quality when performing a resource allocation and distributes quality-domain penalties proportional to the difference between each videodecoding task's maximum and minimum quality requirements. We compare the KSBS with other fairness policies in the literature and find that, because it explicitly considers multimedia quality, it provides significantly fairer resource allocations in terms of the resulting PSNR compared with policies that operate solely in the resource domain. To weight the quality impact of the resource allocations to the different decoding tasks depending on application-specific requirements or user preferences, we generalize the existing KSBS solution by introducing bargaining powers based on each video sequence's motion and texture characteristics.
We propose a new complexity modeling framework for multimedia tasks. We characterize the traffic with five parameters that together we designate as a task's complexity specification (CSPEC). We extend this model t...
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ISBN:
(纸本)9781424417650
We propose a new complexity modeling framework for multimedia tasks. We characterize the traffic with five parameters that together we designate as a task's complexity specification (CSPEC). We extend this model to a scalable CSPEC, which can be used to characterize the many complexity- and quality-scalable operating points available to multimedia tasks. The proposed scalable CSPEC can be used by multimedia applications to match their resource requirements to available system resources.
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 conside...
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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 videodecoding system, significant system performance gains on the order of 4 dB peak-signal-to-noise ratio can be achieved.
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