A Novel Adaptive 360° Livestreaming with Graph Representation Learning based FoV Prediction

The exceptionally high bandwidth requirements associated with the delivery of live 360° video content pose significant challenges in the current network context. An avenue for addressing this bandwidth challenge is to use the limited network resources for sending the user's Field-of-View (FoV)...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing 2024-08, p.1-14
Main Authors: Chen, Xingyan, Du, Huaming, Wang, Mu, Zhao, Yu, Shu, Xiaoyang, Xu, Changqiao, Muntean, Gabriel-Miro
Format: Article
Language:English
Subjects:
360° live video streaming
adaptive streaming
Bandwidth
Clustering algorithms
FoV prediction
graph representation learning
Heuristic algorithms
Prediction algorithms
Representation learning
Servers
Streaming media
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Summary:The exceptionally high bandwidth requirements associated with the delivery of live 360° video content pose significant challenges in the current network context. An avenue for addressing this bandwidth challenge is to use the limited network resources for sending the user's Field-of-View (FoV) tiles at a high resolution, instead of transmitting all frame components at high quality. However, precisely forecasting the FoV for 360° live video content distribution remains a complex endeavor due to the lack of pre-knowledge on user viewing behaviors. In this paper, we present GL360, a novel 360° transmission framework, which employs Graph Representation Learning for FoV prediction. First, we analyze the interaction between users and tiles in panoramic videos utilizing a dynamic heterogeneous Relational Graph Convolutional Network (RGCN), which facilitates efficient user and tile embedding representation learning. Secondly, we propose an online dynamic heterogeneous graph learning (DHGL)-based algorithm to dynamically capture the time-varying features of the user's viewing behaviors with limited prior knowledge. Further, we design a FoV-aware content delivery algorithm that allows the edge servers to determine the video tiles' resolution for each accessed user. Experimental results based on real traces demonstrate how our solution outperforms four other solutions in terms of FoV prediction and network performance
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2024.3435002