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Uncoded Multimedia Transmission [Kõva köide]

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  • Formaat: Hardback, 326 pages, kõrgus x laius: 234x156 mm, kaal: 580 g, 9 Tables, black and white; 96 Line drawings, black and white; 32 Halftones, black and white; 128 Illustrations, black and white
  • Sari: Multimedia Computing, Communication and Intelligence
  • Ilmumisaeg: 22-Jul-2021
  • Kirjastus: CRC Press
  • ISBN-10: 0367632950
  • ISBN-13: 9780367632953
Teised raamatud teemal:
Uncoded Multimedia TransmissionSuurem pilt
  • Formaat: Hardback, 326 pages, kõrgus x laius: 234x156 mm, kaal: 580 g, 9 Tables, black and white; 96 Line drawings, black and white; 32 Halftones, black and white; 128 Illustrations, black and white
  • Sari: Multimedia Computing, Communication and Intelligence
  • Ilmumisaeg: 22-Jul-2021
  • Kirjastus: CRC Press
  • ISBN-10: 0367632950
  • ISBN-13: 9780367632953
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367632953.html
Märksõnad:

An uncoded multimedia transmission (UMT) system is one that skips quantization and entropy coding in compression and all subsequent binary operations, including channel coding and bit-to-symbol mapping of modulation. By directly transmitting non-binary symbols with amplitude modulation, the uncoded system avoids the annoying cliff effect observed in the coded transmission system. This advantage makes uncoded transmission more suited to both unicast in varying channel conditions and multicast to heterogeneous users. In Part I of this book we consider how to improve the efficiency of uncoded transmission and make it on par with the coded transmission. In Part II, we discuss three technologies for multimedia correlation processing in uncoded transmission – Cactus, DCast and LineCast.

All the three pieces of work demonstrate the possibility to build a more robust and efficient wireless multimedia communication system than existing digital ones. In fact, the efficiency of a transmission system is decided by how the resources, including bandwidth, power, and subchannel, are allocated. In Part III, we address the resource allocation problem for UVT in a Rayleigh fading channel, where only statistical channel state information (CSI) is available to the sender. Based on the observation that discarding low-priority (LP) data and saving the channel uses for high-priority (HP) data can significantly improve the quality of the received video, we formulate an optimization problem that aims to minimize the total squared error of a multi-variant Gaussian random vector and find a near optimal solution. Furthermore, the resource allocation problem for UVT is also studied in Non-Orthogonal Multiple Access (NOMA) systems.  

In Part IV, we propose ParCast+ which first separates the source and the channel into independent components, matches the more important source components with higher-gain channel components, and uses amplitude modulation for transmission. In this part of the book, we also consider image and video delivery in MIMO broadcasting networks with diverse channel quality and varying numbers of antennas across receivers In the last part of this book, we investigate the cases where analog transmission can be used in conjunction with digital transmission for a balanced efficiency and adaptation capability. In such a hybrid digital-analog (HDA) system, the two key questions we shall answer are how to separate the video signal into digital and analog parts and how to allocate limited resources between and within digital and analog transmissions.

This book may be used as a collection of research notes for researchers in this field, a reference book for practitioners or engineers, as well as a textbook for a graduate advanced seminar in this fieldor any related fields. The references collected in this book may be used as further reading lists or references for the readers.

Preface xiii
Acknowledgments xvii
Acronyms xix
Part I Video Transmission - Coded or Uncoded
1 Uncoded Video Transmission
3(14)
1.1 Coded Video Transmission
3(3)
1.2 Uncoded Video Transmission
6(8)
1.2.1 Basic Concept
6(1)
1.2.2 Theoretical Work
7(2)
1.2.3 SoftCast
9(5)
1.3 Challenges in UVT
14(3)
2 Advances in Uncoded and Hybrid Multimedia Transmission
17(14)
2.1 Advances in Uncoded Multimedia Transmission
17(6)
2.1.1 Multimedia Correlation Processing
18(1)
2.1.2 Resource Allocation
19(2)
2.1.3 MIMO Support
21(2)
2.2 Advances in HDA Multimedia Transmission
23(3)
2.2.1 Theoretical Work
25(1)
2.3 Summary
26(5)
Part II Correlation Processing
3 Keeping Redundancy in Transmission
31(16)
3.1 Introduction
31(1)
3.2 Overview of the Proposed System
32(2)
3.3 Resource Allocation for Spatial-Domain Transmission
34(4)
3.3.1 Bandwidth Allocation
34(2)
3.3.2 Power Allocation
36(2)
3.4 Implementation
38(2)
3.4.1 Sender
39(1)
3.4.2 Receiver
40(1)
3.5 Evaluation
40(5)
3.5.1 Methodology
40(2)
3.5.2 System Comparison
42(3)
3.6 Summary
45(2)
4 Distributed Uncoded Video Transmission
47(24)
4.1 Introduction
47(1)
4.2 Proposed DCast
48(8)
4.2.1 Coset Coding
50(1)
4.2.2 Coset Quantization
51(1)
4.2.3 Power Allocation
52(1)
4.2.4 Packaging and Transmission
53(1)
4.2.5 LMMSE Decoding
54(2)
4.3 Power-distortion Optimization
56(4)
4.3.1 Relationship between Variables
56(1)
4.3.2 MV Transmission Power and Distortion
57(1)
4.3.3 MV Distortion and Prediction Noise Variance
58(1)
4.3.4 Distortion Formulation
59(1)
4.3.5 Solution
59(1)
4.4 Experiments
60(9)
4.4.1 PDO Model Verification
62(1)
4.4.2 Unicast Performance
62(2)
4.4.3 Robustness Test
64(3)
4.4.4 Multicast Performance
67(1)
4.4.5 Complexity and Bit Rate
68(1)
4.5 Summary
69(2)
5 Line-based Uncoded Image Transmission
71(20)
5.1 Introduction
71(2)
5.2 The Proposed LineCast
73(6)
5.2.1 1D Transform
74(1)
5.2.2 Scalar Modulo Quantization
74(3)
5.2.3 Power Allocation and Transmission
77(1)
5.2.4 LLSE Decoder
78(1)
5.2.5 Side Information Generation
78(1)
5.2.6 MMSE Denoising
79(1)
5.3 Bandwidth Expansion and Compression
79(1)
5.4 Experimental Results
80(4)
5.4.1 LineCast Performance
81(1)
5.4.2 Broadcast Results
81(2)
5.4.3 Bandwidth Expansion
83(1)
5.4.4 Visual Quality
83(1)
5.5 Summary
84(7)
Part III Resource Allocation
6 Joint Bandwidth and Power Allocation
91(20)
6.1 Introduction
91(1)
6.2 Problem
92(2)
6.2.1 System Model
92(2)
6.2.2 Problem Statement
94(1)
6.3 Analysis
94(6)
6.3.1 Power Allocation Problem
95(2)
6.3.2 Bandwidth Allocation Problem
97(3)
6.4 Solution
100(4)
6.4.1 An Iterative Algorithm
100(2)
6.4.2 Proposed Fast Algorithm
102(2)
6.5 Evaluation
104(6)
6.5.1 Implementation
104(1)
6.5.2 Settings
105(1)
6.5.3 Results
106(4)
6.6 Summary
110(1)
7 Progressive Transmission
111(18)
7.1 Introduction
111(2)
7.2 Progressive Uncoded Video Transmission
113(3)
7.2.1 Framework Overview
113(2)
7.2.2 System Model and Problem Formulation
115(1)
7.3 The Proposed Solution
116(6)
7.3.1 Power Allocation
116(2)
7.3.2 Scheduling
118(2)
7.3.3 Approximation
120(2)
7.4 Evaluation
122(5)
7.4.1 Settings
122(1)
7.4.2 Results in Simulated Environment
123(2)
7.4.3 Trace-Driven Emulation
125(2)
7.5 Summary
127(2)
8 Superposed Transmission with NOMA
129(18)
8.1 Introduction
129(2)
8.2 System Description
131(3)
8.2.1 SoftCast-based Video Encoding with SC
132(1)
8.2.2 Video Reconstruction with SIC and LLSE
133(1)
8.3 Problem Formulation and Analysis
134(4)
8.3.1 Problem Statement and Formulation
134(1)
8.3.2 Two-stage Power Allocation
135(2)
8.3.3 Two-sided Matching Formulation for Chunk Scheduling
137(1)
8.4 Matching Algorithm for Chunk Scheduling
138(3)
8.4.1 Design and Description of Algorithm
138(1)
8.4.2 Analysis of Algorithm
139(2)
8.5 Performance Evaluation
141(4)
8.5.1 Performance Comparison
141(2)
8.5.2 Impacts of Bandwidth Compression Ratio β
143(1)
8.5.3 Impacts of Chunk Size
144(1)
8.6 Summary
145(2)
9 Joint Subcarrier Matching and Power Allocation
147(28)
9.1 Introduction
147(2)
9.2 System Model
149(5)
9.2.1 Overview of SSRVB
150(1)
9.2.2 Spatial Decomposition
150(2)
9.2.3 Robust Video Transmission
152(1)
9.2.4 Spatial Scalability Analysis
153(1)
9.3 Joint Subcarrier Matching and Power Allocation
154(9)
9.3.1 Problem Formulation
154(1)
9.3.2 Power Allocation
155(3)
9.3.3 Subcarrier Matching
158(3)
9.3.4 Iterative Solution
161(1)
9.3.5 Channel State Information Feedback
162(1)
9.4 Performance Evaluation
163(7)
9.4.1 Reference Schemes
164(1)
9.4.2 Results of Spatial Scalability and Joint Resource Allocation
165(2)
9.4.3 Results under Single User Scenarios
167(1)
9.4.4 Results under Multiple Users Scenarios
168(2)
9.4.5 Computation Cost Comparison
170(1)
9.5 Summary
170(5)
Part IV MIMO Support
10 Channel Allocation
175(24)
10.1 Introduction
175(2)
10.2 Background and Motivation
177(2)
10.2.1 Source Characteristics
177(1)
10.2.2 Channel Characteristics
178(1)
10.2.3 Source-channel Similarities
178(1)
10.3 System Design
179(8)
10.3.1 Overview
179(1)
10.3.2 Source Decorrelation
180(1)
10.3.3 Channel Decorrelation
181(2)
10.3.4 Unequal Error Protection for the Coefficients
183(2)
10.3.5 Managing Metadata
185(1)
10.3.6 The Video Decoder
186(1)
10.4 Implementation
187(2)
10.4.1 ParCast+ Implementation
187(1)
10.4.2 Schemes for Comparison
188(1)
10.5 Evaluation
189(8)
10.5.1 Experimental Setup
189(2)
10.5.2 ParCast+ Microbenchmarks
191(4)
10.5.3 Comparison against Alternative Schemes
195(2)
10.6 Summary
197(2)
11 Compressive Sampling Code
199(16)
11.1 Introduction
199(1)
11.2 Compressive Image Broadcasting
200(2)
11.3 Sender Design
202(4)
11.3.1 Power Allocation
202(3)
11.3.2 Compressive Sampling and Transmission
205(1)
11.4 Receiver Design
206(2)
11.4.1 CS Decoder
206(2)
11.5 Simulation Evaluation
208(6)
11.5.1 Comparison with SoftCast
208(1)
11.5.2 Comparison with Conventional Digital Schemes
209(3)
11.5.3 Overall Performance in a Broadcasting Session
212(2)
11.6 Summary
214(1)
12 Multiple Similar Description Code
215(20)
12.1 Introduction
215(1)
12.2 Intuition
216(3)
12.2.1 Basic Idea
217(1)
12.2.2 Innovations
218(1)
12.3 AirScale System Design
219(5)
12.3.1 Generating MSD Sequences
220(1)
12.3.2 Transform and Power Allocation
220(2)
12.3.3 M-STBC Code Construction
222(1)
12.3.4 Reconstruction Algorithm
223(1)
12.4 Evaluation
224(6)
12.4.1 Implementation
224(2)
12.4.2 Environment and Settings
226(1)
12.4.3 System Comparisons
227(1)
12.4.4 Robustness to Radio Failures
228(2)
12.5 Summary
230(5)
Part V Hybrid Digital and Analog Transmission
13 A Practical HDA Design
235(18)
13.1 Introduction
235(1)
13.2 The Proposed HDA Framework
236(2)
13.3 Optimization in Resource Allocation
238(5)
13.3.1 Problem Formulation
238(1)
13.3.2 Problem Analysis
239(4)
13.4 A Practical Design
243(4)
13.5 Implementation and Evaluation
247(5)
13.5.1 Implementation
247(1)
13.5.2 Settings
248(1)
13.5.3 Results
248(4)
13.6 Summary
252(1)
14 Structure-Preserving Hybrid Digital-Analog Transmission
253(20)
14.1 Introduction
253(2)
14.2 SharpCast System Design
255(5)
14.2.1 Overview
255(2)
14.2.2 Video Decomposition
257(2)
14.2.3 Digital Processing and Transmission
259(1)
14.2.4 Analog Processing and Transmission
260(1)
14.3 Resource Allocation
260(7)
14.3.1 Problem Formulation
260(2)
14.3.2 The Proposed Solution
262(1)
14.3.3 Solving Sub-problem 1
263(2)
14.3.4 Solving Sub-problem 2
265(2)
14.4 Evaluation and Results
267(4)
14.4.1 Methodology
267(1)
14.4.2 Benchmark Evaluation
268(1)
14.4.3 Performance Comparison
269(2)
14.5 Summary
271(2)
15 Superimposed Modulation for Soft Video Delivery with Hidden Resources
273(20)
15.1 Introduction
273(2)
15.2 Soft Video Delivery with HDA-SIM
275(5)
15.2.1 An Overview of the Soft Video Delivery Framework
275(1)
15.2.2 Introduction of HDA-SIM
276(2)
15.2.3 Analysis of HDA-SIM
278(2)
15.3 Resource Allocation in HDA-SIM
280(5)
15.3.1 Problem Formulation and Definitions
280(2)
15.3.2 Channel Allocation
282(1)
15.3.3 Power Allocation
283(2)
15.4 Implementations
285(1)
15.4.1 SoftCast-SIM
285(1)
15.4.2 SharpCast-SIM
285(1)
15.5 Evaluations
286(5)
15.5.1 Settings
286(1)
15.5.2 Benchmark Evaluations of HDA-SIM
286(2)
15.5.3 Performance Comparison
288(2)
15.5.4 Trace-driven Emulations
290(1)
15.6 Summary
291(2)
16 Adaptive HDA Video Transmission in Mobile Networks
293(22)
16.1 Introduction
293(1)
16.2 System Overview
294(3)
16.2.1 Digital Encoder
295(1)
16.2.2 Packaging and Modulation
296(1)
16.2.3 Maintaining the Integrity of the Specifications
296(1)
16.3 Effect of Channel Prediction on Video Transmission in Mobile Networks
297(3)
16.3.1 Long-range Prediction Algorithm
297(1)
16.3.2 Video Content Division Strategy
298(1)
16.3.3 Time Complexity of Proposed System
299(1)
16.4 P-APDO in Single-user Scenarios
300(3)
16.4.1 Power Allocation Strategy in Hybrid Digital-Analog Transmission
300(1)
16.4.2 Chunk-based Power Allocation
301(1)
16.4.3 Subband-based Adaptive Power Distortion Optimization
302(1)
16.5 P-APDO in Multi-user Scenarios
303(4)
16.5.1 Multi-user Power Allocation Strategy in Hybrid Digital-Analog Transmission
304(1)
16.5.2 Chunk-based Power Pre-allocation for Multi-user Parallel Transmission
305(1)
16.5.3 Power Re-allocation among Chunks Being Transmitted
306(1)
16.5.4 Subband-based Adaptive Power Distortion Optimization
307(1)
16.6 Performance Evaluation
307(6)
16.6.1 Simulation Results in Single-user Scenarios
310(2)
16.6.2 Simulation Results in Multi-user Scenarios
312(1)
16.7 Summary
313(2)
References 315(10)
Index 325
Feng Wu received his B.S. degree in Electrical Engineering from XIDIAN University in 1992. He received his M.S. and Ph.D. degrees in Computer Science from Harbin Institute of Technology in 1996 and 1999, respectively. Now he is a full professor, the assistant to the president of University of Science and Technology of China (USTC) and the director of National Engineering Laboratory of Brain-Inspired Intelligence Technology and Application (NEL-BITA). Before that, he was a principle researcher and a research manager in Microsoft Research Asia. His research interests include image and video compression, media communication, and media analysis and synthesis. He has authored or co-authored over 300 high quality papers (including about 100 Journal papers) and top conference papers on MOBICOM, SIGIR, CVPR and ACM MM. He has 80 granted US patents. His 15 techniques have been adopted into international video coding standards. His work in Google Scholar has been cited 18000+ (H-index as 54) to date. As a co-author, he got the best paper award in IEEE T-CSVT 2009, IEEE VCIP 2016, PCM 2008 and SPIE VCIP 2007. Wu has been a Fellow of IEEE. He serves or served as, EIC of TCSVT, DEiC of TCSVT, Associate Editors in IEEE TIP, IEEE TCSVT, IEEE TMM, and several other International journals. He also serves/served as General Chair in ICME 2019, TPC Chair in MMSP 2011, VCIP 2010 and PCM 2009.

Dr. Chong Luo joined Microsoft Research Asia (MSRA) in 2003, where she is currently a Principal Researcher with the Intelligent Multimedia Group. She is also an Adjunct Professor and Ph.D. advisor with the University of Science and Technology of China (USTC). Dr. Luo received her Ph.D. degree in Electrical Engineering from Shanghai Jiao Tong University in 2012, M.Sc. degree in Computer Science from National University of Singapore (NUS), Singapore in 2002 and B.Sc. degree in Computer Science from Fudan University, China in 2000. She has been an IEEE senior member since 2014. Dr. Luo has been working on various video-related topics, including peer-to-peer video conferencing, wireless video communications, and multimedia cloud computing. Her current research focus is on building intelligent multimedia systems based on advanced AI technologies.

Hancheng Lu received his Ph.D. degree from University of Science and Technology of China (USTC) in July 2005, in Communication and Information Systems. He has been a faculty member of USTC since July 2005 and worked as an associate professor at USTC since Jan. 2008. His research interests include multimedia communication and networking, resource optimization in wireless heterogeneous networks. Lu is active in academic volunteer work. He has served as a reviewer for IEEE JSAC, IEEE TWC, IEEE TMM, IEEE T-CSVT, and Technical Program Committee (TPC) member at IEEE ICC, IEEE GLOBECOM, IEEE WCNC.