论文标题
自适应视频流的个性化QOE增强：一种数字双辅助方案
Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme
论文作者
论文摘要
在本文中，我们提出了一个数字双胞胎（DT）辅助自适应视频流方案，以增强个性化体验质量（PQOE）。由于PQOE模型是特定于用户的，并且具有时间变化，因此基于通用和时间不变的PQOE模型的现有方案可能会遭受性能降解。为了解决此问题，我们首先提出了一种DT辅助的PQOE模型构建方法，以获得准确的用户特异性PQOE模型。具体而言，用户DTS（UDT）分别是为单个用户构建的，这些用户可以获取和利用用户的数据实时准确调整PQOE模型参数。接下来，考虑到获得的PQOE模型，我们通过将用户位置，视频内容请求和缓冲区状态的动态考虑在内，制定了资源管理问题，以最大程度地提高整体长期PQOE。为了解决这个问题，开发了深入的增强学习算法，以共同确定段版本选择以及通信和计算资源分配。对现实世界数据集的仿真结果表明，与基准方案相比，所提出的方案可以有效地增强PQOE。
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.
