  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.
