A 3D Framework for Improving Low-Latency Multi-Channel Live Streaming

TL;DR

This paper introduces a 3D virtual environment framework using game engines to optimize multi-channel live streaming, significantly reducing latency and improving user interaction in real-time video applications.

Contribution

The paper presents a novel 3D environment-based framework that consolidates multi-camera streams into a single stream, enhancing efficiency and flexibility in low-latency live streaming.

Findings

Reduces streaming latency compared to existing solutions

Supports flexible multi-channel configurations like one-to-many and one-to-one

Outperforms traditional methods in responsiveness and user interaction

Abstract

The advent of 5G has driven the demand for high-quality, low-latency live streaming. However, challenges such as managing the increased data volume, ensuring synchronization across multiple streams, and maintaining consistent quality under varying network conditions persist, particularly in real-time video streaming. To address these issues, we propose a novel framework that leverages 3D virtual environments within game engines (e.g., Unity 3D) to optimize multi-channel live streaming. Our approach consolidates multi-camera video data into a single stream using multiple virtual 3D canvases, significantly increasing channel amounts while reducing latency and enhancing user flexibility. For demonstration of our approach, we utilize the Unity 3D engine to integrate multiple video inputs into a single-channel stream, supporting one-to-many broadcasting, one-to-one video calling, and real-time control of video channels. By mapping video data onto a world-space canvas and capturing it via an in-world camera, we minimize redundant data transmission, achieving efficient, low-latency streaming. Our results demonstrate that this method outperforms some existing multi-channel live streaming solutions in both latency reduction and user interaction responsiveness improvement. Our live video streaming system affiliated with this paper is also open-source at https://github.com/Aizierjiang/LiveStreaming.