A Review of Deep Learning Solutions in 360° Video Streaming

The spread of virtual reality and 360° video applications has raised research interest in developing new streaming techniques. On one hand, 360° videos rely on strict network requirements compared to conventional 2D videos. Realizing an adequate user experience is subject to ultra-low latency and hu...

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Bibliographic Details
Published in2023 12th International Conference on Modern Circuits and Systems Technologies (MOCAST) pp. 1 - 4
Main Authors Mahmoud, Moatasim, Rizou, Stamatia, Panayides, Andreas S., Lazaridis, Pavlos I., Kantartzis, Nikolaos V., Karagiannidis, George K., Zaharis, Zaharias D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.06.2023
Subjects
360° videos
augmented reality (AR)
Bit rate
Circuits and systems
Deep learning
Predictive models
Solid modeling
Streaming media
video streaming
Virtual reality
virtual reality (VR)
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Summary:The spread of virtual reality and 360° video applications has raised research interest in developing new streaming techniques. On one hand, 360° videos rely on strict network requirements compared to conventional 2D videos. Realizing an adequate user experience is subject to ultra-low latency and huge bitrate requirements. On the other hand, 360° videos have distinct characteristics that allow for innovative streaming solutions. These solutions have benefited from the advancements in deep learning for optimizing the transmission under restricted network resources. In this paper, we review existing works employing deep learning in 360° video transmission and we highlight the challenges associated with 360° video streaming.
DOI:10.1109/MOCAST57943.2023.10176729