A Novel Adaptive 360 Livestreaming With Graph Representation Learning Based FoV Prediction

The exceptionally high bandwidth requirements associated with the delivery of live <inline-formula><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> 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 <u>R</u>elational <u>G</u>raph <u>C</u>onvolutional <u>N</u>etwork (RGCN), which facilitates efficient user and tile embedding representation learning. Second, 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.