Enabling User Intent-based Network Path Adaptation for Live Volumetric Streaming

Volumetric video is an emerging media application that enables the projection of people or objects into a virtual space in a real-time and immersive manner. Unlike traditional video, live volumetric video streaming in virtual space allows users to interact with teleported objects with a wide range of intentions. However, a significant technical challenge in this context is the need for real-time network adaptation driven by diverse user intents (e.g., user's movement in the virtual space), which may instantly change the streaming's network demand. To ensure a satisfactory perceived Quality of Experience (QoE) in the face of both intent and network condition uncertainty, we have developed a novel solution framework that allows offline user intent registration and online user intent capture with necessary path adaptation. This path adaptation module is empowered by a novel Multi-Arm Bandit (MAB) based path selection algorithm, with joint consideration of probed application delay and network congestion. Through real and extensive experiments, we have validated the effectiveness of our proposed framework in assuring user QoE under various network conditions and for different user intent scenarios.