Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network

Live virtual reality (VR) streaming (a.k.a., 360-degree video streaming) has become increasingly popular because of the rapid growth of head‐mounted displays and 5G networking deployment. However, the huge bandwidth and the energy required to deliver live VR frames in the wireless video sensor netwo...

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Autores principales:	Xiaolei Chen, Baoning Cao, Ishfaq Ahmad
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/4b2c216a1f1e44b9b7d211625b8ba262
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id	oai:doaj.org-article:4b2c216a1f1e44b9b7d211625b8ba262
record_format	dspace
spelling	oai:doaj.org-article:4b2c216a1f1e44b9b7d211625b8ba2622021-11-22T01:11:27ZLightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network1875-905X10.1155/2021/8501990https://doaj.org/article/4b2c216a1f1e44b9b7d211625b8ba2622021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8501990https://doaj.org/toc/1875-905XLive virtual reality (VR) streaming (a.k.a., 360-degree video streaming) has become increasingly popular because of the rapid growth of head‐mounted displays and 5G networking deployment. However, the huge bandwidth and the energy required to deliver live VR frames in the wireless video sensor network (WVSN) become bottlenecks, making it impossible for the application to be deployed more widely. To solve the bandwidth and energy challenges, VR video viewport prediction has been proposed as a feasible solution. However, the existing works mainly focuses on the bandwidth usage and prediction accuracy and ignores the resource consumption of the server. In this study, we propose a lightweight neural network-based viewport prediction method for live VR streaming in WVSN to overcome these problems. In particular, we (1) use a compressed channel lightweight network (C-GhostNet) to reduce the parameters of the whole model and (2) use an improved gate recurrent unit module (GRU-ECA) and C-GhostNet to process the video data and head movement data separately to improve the prediction accuracy. To evaluate the performance of our method, we conducted extensive experiments using an open VR user dataset. The experiments results demonstrate that our method achieves significant server resource saving, real-time performance, and high prediction accuracy, while achieving low bandwidth usage and low energy consumption in WVSN, which meets the requirement of live VR streaming.Xiaolei ChenBaoning CaoIshfaq AhmadHindawi LimitedarticleTelecommunicationTK5101-6720ENMobile Information Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Xiaolei Chen Baoning Cao Ishfaq Ahmad Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network
description	Live virtual reality (VR) streaming (a.k.a., 360-degree video streaming) has become increasingly popular because of the rapid growth of head‐mounted displays and 5G networking deployment. However, the huge bandwidth and the energy required to deliver live VR frames in the wireless video sensor network (WVSN) become bottlenecks, making it impossible for the application to be deployed more widely. To solve the bandwidth and energy challenges, VR video viewport prediction has been proposed as a feasible solution. However, the existing works mainly focuses on the bandwidth usage and prediction accuracy and ignores the resource consumption of the server. In this study, we propose a lightweight neural network-based viewport prediction method for live VR streaming in WVSN to overcome these problems. In particular, we (1) use a compressed channel lightweight network (C-GhostNet) to reduce the parameters of the whole model and (2) use an improved gate recurrent unit module (GRU-ECA) and C-GhostNet to process the video data and head movement data separately to improve the prediction accuracy. To evaluate the performance of our method, we conducted extensive experiments using an open VR user dataset. The experiments results demonstrate that our method achieves significant server resource saving, real-time performance, and high prediction accuracy, while achieving low bandwidth usage and low energy consumption in WVSN, which meets the requirement of live VR streaming.
format	article
author	Xiaolei Chen Baoning Cao Ishfaq Ahmad
author_facet	Xiaolei Chen Baoning Cao Ishfaq Ahmad
author_sort	Xiaolei Chen
title	Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network
title_short	Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network
title_full	Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network
title_fullStr	Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network
title_full_unstemmed	Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network
title_sort	lightweight neural network-based viewport prediction for live vr streaming in wireless video sensor network
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/4b2c216a1f1e44b9b7d211625b8ba262
work_keys_str_mv	AT xiaoleichen lightweightneuralnetworkbasedviewportpredictionforlivevrstreaminginwirelessvideosensornetwork AT baoningcao lightweightneuralnetworkbasedviewportpredictionforlivevrstreaminginwirelessvideosensornetwork AT ishfaqahmad lightweightneuralnetworkbasedviewportpredictionforlivevrstreaminginwirelessvideosensornetwork
_version_	1718418280329248768

Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network

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