Robust QoE-Driven DASH Over OFDMA Networks
Robust QoE-Driven DASH Over OFDMA Networks
复制标题
DOI：
10.1109/tmm.2019.2929929
复制
发表时间：
2020-02
影响因子：
7.3
通讯作者：
Kefan Xiao;S. Mao;Jitendra Tugnait
中科院分区：
文献类型：
--
作者：
Kefan Xiao;S. Mao;Jitendra Tugnait
本文研究了在正交频分复用接入（OFDMA）网络上动态自适应流媒体（DASH）视频的有效鲁棒传输问题。在一项测量研究的激励下，我们提出探索DASH over OFDMA的请求间隔和鲁棒速率预测。我们首先基于一种新的体验质量（QoE）模型提出了一个离线跨层优化问题。然后推导了在线重构公式，并证明了其渐近最优性。在分析了在线问题的结构后，提出了一种分解方法，得到了用户设备（UE）率自适应问题和BS资源分配问题。我们引入随机模型预测控制（SMPC）来实现高鲁棒性的视频速率自适应，并考虑请求间隔以更有效地分配资源。大量的仿真表明，与其他变体和基准算法相比，该方案可以获得更好的QoE性能，这主要归功于其更低的再缓冲率和更稳定的比特率选择。
In this paper, the problem of effective and robust delivery of Dynamic Adaptive Streaming over HTTP (DASH) videos over an orthogonal frequency-division multiplexing access (OFDMA) network is studied. Motivated by a measurement study, we propose to explore the request interval and robust rate prediction for DASH over OFDMA. We first formulate an offline cross-layer optimization problem based on a novel quality of experience (QoE) model. Then the online reformulation is derived and proved to be asymptotically optimal. After analyzing the structure of the online problem, we propose a decomposition approach to obtain a user equipment (UE) rate adaptation problem and a BS resource allocation problem. We introduce stochastic model predictive control (SMPC) to achieve high robustness on video rate adaption and consider the request interval for more efficient resource allocation. Extensive simulations show that the proposed scheme can achieve a better QoE performance compared with other variations and a benchmark algorithm, which is mainly due to its lower rebuffering ratio and more stable bitrate choices.