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.
Author(s): Feng Wu, Chong Luo, Hancheng Lu
Series: Series in Multimedia Computing, Communication and Intelligence
Publisher: CRC Press
Year: 2021
Language: English
Pages: 344
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Acknowledgments
Acronyms
Part I: Video Transmission - Coded or Uncoded
1. Uncoded Video Transmission
1.1. Coded Video Transmission
1.2. Uncoded Video Transmission
1.2.1. Basic Concept
1.2.2. Theoretical Work
1.2.3. SoftCast
1.3. Challenges in UVT
2. Advances in Uncoded and Hybrid Multimedia Transmission
2.1. Advances in Uncoded Multimedia Transmission
2.1.1. Multimedia Correlation Processing
2.1.2. Resource Allocation
2.1.3. MIMO Support
2.2. Advances in HDA Multimedia Transmission
2.2.1. Theoretical Work
2.3. Summary
Part II: Correlation Processing
3. Keeping Redundancy in Transmission
3.1. Introduction
3.2. Overview of the Proposed System
3.3. Resource Allocation for Spatial-Domain Transmission
3.3.1. Bandwidth Allocation
3.3.2. Power Allocation
3.4. Implementation
3.4.1. Sender
3.4.2. Receiver
3.5. Evaluation
3.5.1. Methodology
3.5.2. System Comparison
3.6. Summary
4. Distributed Uncoded Video Transmission
4.1. Introduction
4.2. Proposed DCast
4.2.1. Coset Coding
4.2.2. Coset Quantization
4.2.3. Power Allocation
4.2.4. Packaging and Transmission
4.2.5. LMMSE Decoding
4.3. Power-distortion Optimization
4.3.1. Relationship between Variables
4.3.2. MV Transmission Power and Distortion
4.3.3. MV Distortion and Prediction Noise Variance
4.3.4. Distortion Formulation
4.3.5. Solution
4.4. Experiments
4.4.1. PDO Model Verification
4.4.2. Unicast Performance
4.4.3. Robustness Test
4.4.4. Multicast Performance
4.4.5. Complexity and Bit Rate
4.5. Summary
5. Line-based Uncoded Image Transmission
5.1. Introduction
5.2. The Proposed LineCast
5.2.1. 1D Transform
5.2.2. Scalar Modulo Quantization
5.2.3. Power Allocation and Transmission
5.2.4. LLSE Decoder
5.2.5. Side Information Generation
5.2.6. MMSE Denoising
5.3. Bandwidth Expansion and Compression
5.4. Experimental Results
5.4.1. LineCast Performance
5.4.2. Broadcast Results
5.4.3. Bandwidth Expansion
5.4.4. Visual Quality
5.5. Summary
Part III: Resource Allocation
6. Joint Bandwidth and Power Allocation
6.1. Introduction
6.2. Problem
6.2.1. System Model
6.2.2. Problem Statement
6.3. Analysis
6.3.1. Power Allocation Problem
6.3.2. Bandwidth Allocation Problem
6.4. Solution
6.4.1. An Iterative Algorithm
6.4.2. Proposed Fast Algorithm
6.5. Evaluation
6.5.1. Implementation
6.5.2. Settings
6.5.3. Results
6.6. Summary
7. Progressive Transmission
7.1. Introduction
7.2. Progressive Uncoded Video Transmission
7.2.1. Framework Overview
7.2.2. System Model and Problem Formulation
7.3. The Proposed Solution
7.3.1. Power Allocation
7.3.2. Scheduling
7.3.3. Approximation
7.4. Evaluation
7.4.1. Settings
7.4.2. Results in Simulated Environment
7.4.3. Trace-Driven Emulation
7.5. Summary
8. Superposed Transmission with NOMA
8.1. Introduction
8.2. System Description
8.2.1. SoftCast-based Video Encoding with SC
8.2.2. Video Reconstruction with SIC and LLSE
8.3. Problem Formulation and Analysis
8.3.1. Problem Statement and Formulation
8.3.2. Two-stage Power Allocation
8.3.3. Two-sided Matching Formulation for Chunk Scheduling
8.4. Matching Algorithm for Chunk Scheduling
8.4.1. Design and Description of Algorithm
8.4.2. Analysis of Algorithm
8.5. Performance Evaluation
8.5.1. Performance Comparison
8.5.2. Impacts of Bandwidth Compression Ratio B
8.5.3. Impacts of Chunk Size
8.6. Summary
9. Joint Subcarrier Matching and Power Allocation
9.1. Introduction
9.2. System Model
9.2.1. Overview of SSRVB
9.2.2. Spatial Decomposition
9.2.3. Robust Video Transmission
9.2.4. Spatial Scalability Analysis
9.3. Joint Subcarrier Matching and Power Allocation
9.3.1. Problem Formulation
9.3.2. Power Allocation
9.3.3. Subcarrier Matching
9.3.4. Iterative Solution
9.3.5. Channel State Information Feedback
9.4. Performance Evaluation
9.4.1. Reference Schemes
9.4.2. Results of Spatial Scalability and Joint Resource Allocation
9.4.3. Results under Single User Scenarios
9.4.4. Results under Multiple Users Scenarios
9.4.5. Computation Cost Comparison
9.5. Summary
Part IV: MIMO Support
10. Channel Allocation
10.1. Introduction
10.2. Background and Motivation
10.2.1. Source Characteristics
10.2.2. Channel Characteristics
10.2.3. Source-channel Similarities
10.3. System Design
10.3.1. Overview
10.3.2. Source Decorrelation
10.3.3. Channel Decorrelation
10.3.4. Unequal Error Protection for the Coefficients
10.3.5. Managing Metadata
10.3.6. The Video Decoder
10.4. Implementation
10.4.1. ParCast+ Implementation
10.4.2. Schemes for Comparison
10.5. Evaluation
10.5.1. Experimental Setup
10.5.2. ParCast+ Microbenchmarks
10.5.3. Comparison against Alternative Schemes
10.6. Summary
11. Compressive Sampling Code
11.1. Introduction
11.2. Compressive Image Broadcasting
11.3. Sender Design
11.3.1. Power Allocation
11.3.2. Compressive Sampling and Transmission
11.4. Receiver Design
11.4.1. CS Decoder
11.5. Simulation Evaluation
11.5.1. Comparison with SoftCast
11.5.2. Comparison with Conventional Digital Schemes
11.5.3. Overall Performance in a Broadcasting Session
11.6. Summary
12. Multiple Similar Description Code
12.1. Introduction
12.2. Intuition
12.2.1. Basic Idea
12.2.2. Innovations
12.3. AirScale System Design
12.3.1. Generating MSD Sequences
12.3.2. Transform and Power Allocation
12.3.3. M-STBC Code Construction
12.3.4. Reconstruction Algorithm
12.4. Evaluation
12.4.1. Implementation
12.4.2. Environment and Settings
12.4.3. System Comparisons
12.4.4. Robustness to Radio Failures
12.5. Summary
Part V: Hybrid Digital and Analog Transmission
13. A Practical HDA Design
13.1. Introduction
13.2. The Proposed HDA Framework
13.3. Optimization in Resource Allocation
13.3.1. Problem Formulation
13.3.2. Problem Analysis
13.4. A Practical Design
13.5. Implementation and Evaluation
13.5.1. Implementation
13.5.2. Settings
13.5.3. Results
13.6. Summary
14. Structure-Preserving Hybrid Digital-Analog Transmission
14.1. Introduction
14.2. SharpCast System Design
14.2.1. Overview
14.2.2. Video Decomposition
14.2.3. Digital Processing and Transmission
14.2.4. Analog Processing and Transmission
14.3. Resource Allocation
14.3.1. Problem Formulation
14.3.2. The Proposed Solution
14.3.3. Solving Sub-problem 1
14.3.4. Solving Sub-problem 2
14.4. Evaluation and Results
14.4.1. Methodology
14.4.2. Benchmark Evaluation
14.4.3. Performance Comparison
14.5. Summary
15. Superimposed Modulation for Soft Video Delivery with Hidden Resources
15.1. Introduction
15.2. Soft Video Delivery with HDA-SIM
15.2.1. An Overview of the Soft Video Delivery Framework
15.2.2. Introduction of HDA-SIM
15.2.3. Analysis of HDA-SIM
15.3. Resource Allocation in HDA-SIM
15.3.1. Problem Formulation and Definitions
15.3.2. Channel Allocation
15.3.3. Power Allocation
15.4. Implementations
15.4.1. SoftCast-SIM
15.4.2. SharpCast-SIM
15.5. Evaluations
15.5.1. Settings
15.5.2. Benchmark Evaluations of HDA-SIM
15.5.3. Performance Comparison
15.5.4. Trace-driven Emulations
15.6. Summary
16. Adaptive HDA Video Transmission in Mobile Networks
16.1. Introduction
16.2. System Overview
16.2.1. Digital Encoder
16.2.2. Packaging and Modulation
16.2.3. Maintaining the Integrity of the Specifications
16.3. Effect of Channel Prediction on Video Transmission in Mobile Networks
16.3.1. Long-range Prediction Algorithm
16.3.2. Video Content Division Strategy
16.3.3. Time Complexity of Proposed System
16.4. P-APDO in Single-user Scenarios
16.4.1. Power Allocation Strategy in Hybrid Digital-Analog Transmission
16.4.2. Chunk-based Power Allocation
16.4.3. Subband-based Adaptive Power Distortion Optimization
16.5. P-APDO in Multi-user Scenarios
16.5.1. Multi-user Power Allocation Strategy in Hybrid Digital-Analog Transmission
16.5.2. Chunk-based Power Pre-allocation for Multi-user Parallel Transmission
16.5.3. Power Re-allocation among Chunks Being Transmitted
16.5.4. Subband-based Adaptive Power Distortion Optimization
16.6. Performance Evaluation
16.6.1. Simulation Results in Single-user Scenarios
16.6.2. Simulation Results in Multi-user Scenarios
16.7. Summary
References
Index
