Deep Reinforcement Learning for MEC Streaming with Joint User Association and Resource Management

Keyphrases

• Resource Management 100%
• User Association 100%
• Deep Reinforcement Learning (deep RL) 100%
• Mobile Edge Computing 100%
• Association Management 100%
• Quality of Experience 66%
• Low Latency 33%
• Optimization Problem 33%
• Number of Users 33%
• Caching 33%
• Limited Resources 33%
• Globally Optimal Solution 33%
• Markov Decision Process 33%
• Polynomial Time 33%
• Gradient Method 33%
• Live Streaming 33%
• Supply-demand 33%
• Selection Problem 33%
• Backhaul 33%
• High Users 33%
• 5G Era 33%
• Nonlinear Integer Programming Model 33%
• Wireless Resources 33%
• Resource Users 33%
• Online Video Streaming 33%
• Deep Deterministic Policy Gradient 33%
• Lagrange Dual Problem 33%
• Video Quality Selection 33%

Engineering

• Joints (Structural Components) 100%
• Reinforcement Learning 100%
• Mobile Edge Computing 100%
• Video Streaming 66%
• Quality of Experience 66%
• Simulation Result 33%
• Optimisation Problem 33%
• Polynomial Time 33%
• Limited Resource 33%
• Lagrange 33%
• Gradient-Based Algorithm 33%
• Markov Decision Process 33%

Computer Science

• Multi-Access Edge Computing 100%
• Deep Reinforcement Learning 100%
• Video Streaming 66%
• Quality-of-Experience 66%
• Polynomial Time 33%
• Optimization Problem 33%
• Globally Optimal Solution 33%
• Integer-Linear Programming 33%
• Problem Selection 33%
• Markov Decision Process 33%
• Dual Problem 33%