Realizing the Metaverse -

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Résumé :

About the Editors xiii

List of Contributors xvii

Foreword xix

Preface xxi

1 Introduction 1
Wei Yang Bryan Lim, Zehui Xiong, Dusit Niyato, Junshan Zhang, and Xuemin Sherman Shen

1.1 Introduction 1

1.1.1 Birth of the Metaverse 1

1.2 Architecture, Developments, and Tools of the Metaverse 4

1.2.1 Definition and Architecture 4

1.2.2 Current Developments 9

1.2.2.1 Massive Multiplayer Online Role-playing Games 10

1.2.2.2 Applications of the Metaverse 11

1.2.3 Tools, Platforms, and Frameworks 13

1.2.4 Summary and Lessons Learned 16

1.2.4.1 The Metaverse Cannot Be Realized by a Standalone Technology 16

1.2.4.2 Implementation Challenges 16

1.2.4.3 Standardized Protocols, Frameworks, and Tools 17

1.2.4.4 Metaverse Ecosystem and Economics 17

2 Communication and Computing in Edge-enabled Metaverse 19
Minrui Xu, Wei Chong Ng, Wei Yang Bryan Lim, and Dusit Niyato

2.1 Introduction 19

2.1.1 Rate-Reliability-Latency 3D Multimedia Networks 21

2.1.1.1 Resource Allocation for VR Streaming 25

2.1.1.2 Resource Allocation for AR Adaptation 28

2.1.1.3 Edge Caching for AR/VR Content 30

2.1.1.4 Hologram Streaming 31

2.1.2 Human-in-the-loop Communication 32

2.1.2.1 URLLC for the Tactile Internet 33

2.1.2.2 Semantic/Goal-aware Communication 34

2.1.3 Real-time Physical-Virtual Synchronization 36

2.1.3.1 Resource Allocation for Physical-Virtual Synchronization 37

2.1.3.2 Intelligent Edge Network-aided Virtual-Physical Synchronization 38

2.1.4 Lessons Learned 40

2.1.4.1 Efficient Immersive Streaming and Interaction 40

2.1.4.2 AI for Intelligent Edge Communication 40

2.1.4.3 Context-aware Immersive Content Delivery 40

2.1.4.4 Self-sustainable Physical-Virtual Synchronization 40

2.1.4.5 Decentralized Incentive Mechanism 41

2.2 Computation 41

2.2.1 The Cloud-Edge-End Computing Paradigm 43

2.2.2 Efficient AR/VR Cloud-Edge-End Rendering 45

2.2.2.1 Stochastic Demand and Network Condition 46

2.2.2.2 Stragglers at Edge Networks 48

2.2.2.3 Heterogeneous Tasks 49

2.2.3 Scalable AI Model Training 49

2.2.3.1 Parameter Pruning and Quantization 50

2.2.3.2 Low-rank Approximation 51

2.2.3.3 Knowledge Distillation 52

2.2.4 Privacy and Security 54

2.2.4.1 Trusted Execution Environment 55

2.2.4.2 Federated Learning 56

2.2.4.3 Adversarial Machine Learning 58

2.2.5 Lessons Learned 60

2.2.5.1 Adaptive AR/VR Cloud-Edge-End Rendering 60

2.2.5.2 On-demand and Generalized Model Compression 60

2.2.5.3 User-centric Computing 60

2.2.5.4 Secure Interoperable Computing 61

2.2.5.5 From Distribution to Decentralization 61

2.3 Summary 61

3 Advanced and Future Network Access Technologies for the Metaverse 63
Andrea Garbugli, Domenico Scotece, Luca Foschini, and Paolo Bellavista

3.1 Introduction 63

3.2 Edge-enabled Metaverse 64

3.2.1 Efficient Service Migration at the Edge 65

3.2.1.1 Multi-layer Container Migration 66

3.2.1.2 Metaverse-enabled Service Migration Architecture 68

3.2.1.3 Simulation Results 71

3.3 Ultra-low Latency Communications 73

3.3.1 ULL Networking Protocols 73

3.3.1.1 Time-sensitive Networking (TSN) 74

3.3.1.2 Wireless Networks: 5G and Wi-Fi 6 74

3.3.2 Acceleration Technologies 76

3.3.2.1 Linux eXpress Data Path (XDP) 76

3.3.2.2 Data Plane Development Kit (DPDK) 77

3.3.2.3 Remote Direct Memory Access (RDMA) 77

3.3.2.4 ...

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