Digital Twin-Assisted Adaptive Preloading for Short Video Streaming

TL;DR

This paper introduces a digital twin-assisted adaptive preloading scheme for short video streaming that predicts throughput and user behavior to optimize bandwidth use and improve user experience.

Contribution

It presents a novel framework combining digital twin technology with adaptive preloading to enhance streaming efficiency and QoE in short videos.

Findings

Improved bandwidth efficiency compared to existing schemes

Accurate throughput and user behavior prediction models

Enhanced user QoE through adaptive preloading strategy

Abstract

We propose a digital twin-assisted adaptive preloading scheme to enhance bandwidth efficiency and user quality of experience (QoE) in short video streaming. We first analyze the relationship between the achievable throughput and video bitrate and critical factors that affect the preloading decision, including the buffer size and bitrate selection. We then construct a digital twin-assisted adaptive preloading framework for short video streaming. By collecting and analyzing historical throughput and tracking behavior information, a throughput prediction model and a probabilistic model can be constructed to accurately predict future throughput and user behavior, respectively. Using the predicted information and real-time running status data from a short video application, we design a preloading strategy to enhance bandwidth efficiency while guaranteeing user QoE. Simulation results demonstrate the effectiveness of our proposed scheme comparing with the state-of-the-art preloading schemes.