Imperial College London
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ProfessorDenizGunduz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Tung:2022:10.1109/jsac.2022.3191354,
author = {Tung, T-Y and Gunduz, D},
doi = {10.1109/jsac.2022.3191354},
journal = {IEEE Journal on Selected Areas in Communications},
pages = {2570--2583},
title = {DeepWiVe: deep-learning-aided wireless video transmission},
url = {http://dx.doi.org/10.1109/jsac.2022.3191354},
volume = {40},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We present DeepWiVe , the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves the video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize the overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect , which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 in terms of the multi-scale structural similarity index measure (MS-SSIM), and H.265+ LDPC by up to 0.0069 on average. We also illustrate the importance of optimizing bandwidth allocation in JSCC video transmission by showing that our optimal bandwidth allocation policy is superior to uniform allocation as well as a heuristic policy benchmark.
AU  - Tung,T-Y
AU  - Gunduz,D
DO  - 10.1109/jsac.2022.3191354
EP  - 2583
PY  - 2022///
SN  - 0733-8716
SP  - 2570
TI  - DeepWiVe: deep-learning-aided wireless video transmission
T2  - IEEE Journal on Selected Areas in Communications
UR  - http://dx.doi.org/10.1109/jsac.2022.3191354
UR  - https://ieeexplore.ieee.org/document/9837870
UR  - http://hdl.handle.net/10044/1/99952
VL  - 40
ER  -
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