Handover Algorithm for Video Communications by Sharing Communication Quality Information of Access Points between Terminals In recent years, the demand for multimedia services that can handle voice andor videos on mobile terminals has increased. Many handover algorithms have been proposed for switching the connection between different networks while maintaining QoE (Quality of Experience), which is a metric that is a measure of usersxe2x80x99 satisfaction when using a certain service or application. The QoEdriven VHO algorithm is one of such algorithms using a prediction model of QoE for movies streamed over wireless networks. Since the algorithm may not provide an acceptable level of quality when network performance temporarily decreases, the authors also proposed an algorithm (hereinafter referred to as the xe2x80x9cprevious algorithmxe2x80x9d) to improve the problem. However, since these algorithms cannot predict QoE of candidate networks before switching, the connection may be switched to a network with much lower QoE. In order to solve the problem, this paper proposes a new handover algorithm for video communications by sharing communication quality information of access points between terminals. In this algorithm, each mobile terminal creates a list specifying the communication quality of the connected access point and shares it with other terminals. The simulation results show that terminals in the proposed method can connect to the access points with better QoE for longer durations than the QoEdriven VHO algorithm and the previous algorithm.

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.