Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme

TL;DR

This paper introduces a digital twin-assisted adaptive video streaming scheme that personalizes quality-of-experience models for users, using real-time data and deep reinforcement learning to optimize resource allocation and significantly improve PQoE.

Contribution

It proposes a novel digital twin-based method for constructing user-specific PQoE models and a deep RL algorithm for resource management in adaptive streaming.

Findings

Enhanced PQoE compared to benchmark schemes

Effective real-time user-specific PQoE modeling

Improved resource allocation via deep reinforcement learning

Abstract

In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE). Since PQoE models are user-specific and time-varying, existing schemes based on universal and time-invariant PQoE models may suffer from performance degradation. To address this issue, we first propose a DT-assisted PQoE model construction method to obtain accurate user-specific PQoE models. Specifically, user DTs (UDTs) are respectively constructed for individual users, which can acquire and utilize users' data to accurately tune PQoE model parameters in real time. Next, given the obtained PQoE models, we formulate a resource management problem to maximize the overall long-term PQoE by taking the dynamics of user' locations, video content requests, and buffer statuses into account. To solve this problem, a deep reinforcement learning algorithm is developed to jointly determine segment version selection, and communication and computing resource allocation. Simulation results on the real-world dataset demonstrate that the proposed scheme can effectively enhance PQoE compared with benchmark schemes.