VARFVV: View-Adaptive Real-Time Interactive Free-View Video Streaming with Edge Computing

TL;DR

VARFVV is a novel edge computing system that enables real-time, high-quality free-view video streaming with low latency and efficient resource use, improving user experience in immersive multi-view content.

Contribution

It introduces a low-complexity view reassembly scheme and a popularity-adaptive bit allocation method using graph neural networks for scalable, efficient free-view video streaming.

Findings

Outperforms existing methods in video quality and latency

Supports over 500 users simultaneously on a single edge server

Achieves a switching delay of 71.5ms

Abstract

Free-view video (FVV) allows users to explore immersive video content from multiple views. However, delivering FVV poses significant challenges due to the uncertainty in view switching, combined with the substantial bandwidth and computational resources required to transmit and decode multiple video streams, which may result in frequent playback interruptions. Existing approaches, either client-based or cloud-based, struggle to meet high Quality of Experience (QoE) requirements under limited bandwidth and computational resources. To address these issues, we propose VARFVV, a bandwidth- and computationally-efficient system that enables real-time interactive FVV streaming with high QoE and low switching delay. Specifically, VARFVV introduces a low-complexity FVV generation scheme that reassembles multiview video frames at the edge server based on user-selected view tracks, eliminating the need for transcoding and significantly reducing computational overhead. This design makes it well-suited for large-scale, mobile-based UHD FVV experiences. Furthermore, we present a popularity-adaptive bit allocation method, leveraging a graph neural network, that predicts view popularity and dynamically adjusts bit allocation to maximize QoE within bandwidth constraints. We also construct an FVV dataset comprising 330 videos from 10 scenes, including basketball, opera, etc. Extensive experiments show that VARFVV surpasses existing methods in video quality, switching latency, computational efficiency, and bandwidth usage, supporting over 500 users on a single edge server with a switching delay of 71.5ms. Our code and dataset are available at https://github.com/qianghu-huber/VARFVV.