Video services have hold a surprising proportion of the whole network traffic in wireless communication networks. Accurate prediction of video traffic can endow networks with intelligence in resource management, especially for the forthcoming beyond the fifth-generation (B5G) networks. However, the existing approaches fail to accurately predict video traffic with all types of frames, due to the natures of strong long-range dependence, self-similarity and burstiness. Obviously, it is unable to meet the QoS and QoE requirements of dynamic bandwidth allocation. In this paper, we propose the feasibility of advanced machine learning methodology applied in nonstationary video traffic prediction, i.e., smoothing-aided support vector machine (SSVM) model. The model utilizes classical smoothing methods to preprocess video traffic by relieving the drastic fluctuation of video stream. It can provide an effective association for the subsequent support vector regression, as the preprocessed data becomes more smooth and continuous than the original unprocessed one. Experimental results show that our proposed model significantly outperforms the state of the art model, i.e., logistic smooth transition autoregressive, in prediction performance. The superior nonlinear approximation capacity is further demonstrated by visualized statistical analysis.

中文翻译：

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基于平滑辅助支持向量机的 B5G 网络非平稳视频流量预测

视频服务在无线通信网络中占据了整个网络流量的惊人比例。视频流量的准确预测可以赋予网络资源管理的智能，特别是对于即将到来的第五代（B5G）网络。然而，由于强长程依赖性、自相似性和突发性的特性，现有方法无法准确预测所有类型帧的视频流量。显然，无法满足动态带宽分配的QoS和QoE要求。在本文中，我们提出了应用于非平稳视频流量预测的先进机器学习方法的可行性，即平滑辅助支持向量机 (SSVM) 模型。该模型利用经典的平滑方法通过缓解视频流的剧烈波动来预处理视频流量。它可以为后续的支持向量回归提供有效的关联，因为预处理的数据比原始未处理的数据变得更加平滑和连续。实验结果表明，我们提出的模型在预测性能方面明显优于最先进的模型，即逻辑平滑过渡自回归。可视化统计分析进一步证明了卓越的非线性逼近能力。实验结果表明，我们提出的模型在预测性能方面明显优于最先进的模型，即逻辑平滑过渡自回归。可视化统计分析进一步证明了卓越的非线性逼近能力。实验结果表明，我们提出的模型在预测性能方面明显优于最先进的模型，即逻辑平滑过渡自回归。可视化统计分析进一步证明了卓越的非线性逼近能力。