The design, implementation and evaluation of SMART: a scheduler for multimedia applications
Proceedings of the sixteenth ACM symposium on Operating systems principles
Real-time dynamic voltage scaling for low-power embedded operating systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hierarchical Adaptive Dynamic Power Management
IEEE Transactions on Computers
A Cross-Layer Approach for Power-Performance Optimization in Distributed Mobile Systems
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 10 - Volume 11
GRACE-1: Cross-Layer Adaptation for Multimedia Quality and Battery Energy
IEEE Transactions on Mobile Computing
Policy optimization for dynamic power management
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Power-rate-distortion analysis for wireless video communication under energy constraints
IEEE Transactions on Circuits and Systems for Video Technology
Online layered learning for cross-layer optimization of dynamic multimedia systems
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
Online reinforcement learning for dynamic multimedia systems
IEEE Transactions on Image Processing
On-line learning and optimization for wireless video transmission
IEEE Transactions on Signal Processing
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In recent years, cross-layer multimedia system design and optimization has garnered significant attention; however, there exists no rigorous methodology for optimizing two or more system layers (e.g., the application, operating system, and hardware layers) jointly while maintaining a separation among the decision processes, designs, and implementations of each layer. Moreover, existing work often relies on myopic optimizations, which ignore the impact of decisions made at the current time on the system's future performance. In this paper, we propose a novel systematic framework for jointly optimizing the different system layers to improve the performance of one multimedia application. In particular, we model the system as a layered Markov decision process (MDP). The proposed layered MDP framework enables each layer to make autonomous and foresighted decisions, which optimize the system's long-term performance.