A PacketLayer Video Quality Assessment Model Based on Spatiotemporal Complexity Estimation Packetlayer video quality assessment model is a lightweight model that is useful for application scenarios like inservice video quality monitoring and network service planning, which is under standardization in ITUT. In this paper we first differentiate the requirements for video quality assessment model from these two different application scenarios. Then different criteria and methods are analyzed and used to construct two types of test datasets for model building and evaluation. Finally, a novel packetlayer video quality assessment model dealing with video quality monitoring is proposed by incorporating the estimation of the spatiotemporal complexity of video content. The interaction between content features and the error concealment effects plus error propagation effects is considered. Experiment results demonstrate that the proposed model achieves preferable and robust performance improvement compared with the existing models in both datasets for scenarios of video quality planning and monitoring. Especially, larger Pearson correlation increase from 0.75 to 0.93 and RMSE decrease from 0.56 to 0.31 are obtained in dataset for video quality monitoring.

A QoEBased Alarm Model for Terminal Video Quality With the advent of the 5G era, quality of experience (QoE) has become one of the most important indicators to measure mobile video quality. If QoE degradation events can be accurately predicted, it will be beneficial for operators to provide better services. However, many factors could affect QoE, including usersxe2x80x99 subjective factors, network and usersxe2x80x99 terminal parameters. This paper proposes a model for QoE degradation alarm in the mobile video stream. Specifically, a largescale dataset is collected in the practical environment by a selfdeveloped application. The dataset is cleaned to reduce the influence of redundancy and outliers firstly. Then, a QoE degradation alarm model is proposed based on datadriven approaches. The random forest is chosen as the core of this model which integrates the advantages of several classification tree and can deal with the highdimensional data. In order to have a better prediction performance, Synthetic Minority Oversampling Technique (SMOTE) is used for solving the imbalance problem of the train set. As is shown by the experimental results, this alarm model can strike a balance between precision and false alarm rate and performance outperforms the stateoftheart methods. This result means the proposed model can save the operational cost while increasing usersxe2x80x99 satisfaction with the mobile video quality.