Traffic descriptors for VBR video teleconferencing over ATM networks
IEEE/ACM Transactions on Networking (TON)
PET—priority encoding transmission (video): a new, robust and efficient video broadcast technology
Proceedings of the third ACM international conference on Multimedia
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Joint video summarization and transmission adaptation for energy-efficient wireless video streaming
EURASIP Journal on Advances in Signal Processing
Relaxations of Weakly Coupled Stochastic Dynamic Programs
Operations Research
Rate-distortion optimized streaming of packetized media
IEEE Transactions on Multimedia
Application-driven cross-layer optimization for video streaming over wireless networks
IEEE Communications Magazine
Near-optimal reinforcement learning framework for energy-aware sensor communications
IEEE Journal on Selected Areas in Communications
Joint Source Adaptation and Resource Allocation for Multi-User Wireless Video Streaming
IEEE Transactions on Circuits and Systems for Video Technology
Peer-to-Peer multimedia sharing based on social norms
Image Communication
QoE-based opportunistic transmission for video broadcasting in heterogeneous circumstance
Proceedings of the 20th ACM international conference on Multimedia
Stochastic game for wireless network virtualization
IEEE/ACM Transactions on Networking (TON)
Energy-aware distributed scheduling for multimedia streaming over Internet of Things
International Journal of Ad Hoc and Ubiquitous Computing
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In this paper, we systematically formulate the problem of multi-user wireless video transmission as a multi-user Markov decision process (MUMDP) by explicitly considering the users' heterogeneous video traffic characteristics, time-varying network conditions as well as, importantly, the dynamic coupling among the users' resource allocations across time, which are often ignored in existing multi-user video transmission solutions. To comply with the decentralized wireless networks' architecture, we propose to decompose the MUMDP into multiple local MDPs using Lagrangian relaxation. Unlike in conventional multi-user video transmission solutions stemming from the network utility maximization framework, the proposed decomposition enables each wireless user to individually solve its own local MDP (i.e. dynamic single-user cross-layer optimization) and the network coordinator to update the Lagrangian multipliers (i.e. resource prices) based on not only current, but also the future resource needs of all users, such that the long-term video quality of all users is maximized. This MUMDP solution provides us the necessary foundations and structures for solving multiuser video communication problems. However, to implement this framework in practice requires statistical knowledge of the experienced environment dynamics, which is often unavailable before transmission time. To overcome this obstacle, we propose a novel online learning algorithm, which allows the wireless users to simultaneously update their policies at multiple states during each time slot. This is different from conventional learning solutions, which often update the current visited state per time slot. The proposed learning algorithm can significantly improve the learning performance, thereby dramatically improving the video quality experienced by the wireless users over time. Our simulation results demonstrate the efficiency of the proposed MUMDP framework as compared to conventional multi-user video transmission solutions.