A method for determining a Quality of Experience associated with a real-time communication session between user devices includes monitoring the real-time communication session and determining at least one quality indicator of the Quality of Experience, at least one first performance indicator of a Quality of Service, and at least one second performance indicator of the Quality of Service. Based on the quality indicator and the first performance indicator, the method determines, among a family of correlation functions indicative of the correlation between the Quality of Experience and a Quality of Service in respect of a generic real-time communication session, a correlation function which is indicative of the correlation between the Quality of Experience and the Quality of Service in respect of the monitored real-time communication session. Then the method applies the first performance indicator and the second performance indicator to the correlation function to determine said Quality of Experience.

A system for troubleshooting network problems is disclosed. A model can use demographic information, network usage information, and network membership information to determine an importance of a problem. The importance of the problem for the user who reported the problem, a number of other users affected by the problem, and the importance of the problem to the other users can be used to determine a priority for resolving the problem. Before and after a work order is executed to resolve the problem, network metrics can be gathered, including aggregate network metrics, and automatically presented in various user interfaces. The analysis of the metrics can be used to update a database of which work orders are assigned in response to which problems.

A system for troubleshooting network problems is disclosed. A model can use demographic information, network usage information, and network membership information to determine an importance of a problem. The importance of the problem for the user who reported the problem, a number of other users affected by the problem, and the importance of the problem to the other users can be used to determine a priority for resolving the problem. Before and after a work order is executed to resolve the problem, network metrics can be gathered, including aggregate network metrics, and automatically presented in various user interfaces. The analysis of the metrics can be used to update a database of which work orders are assigned in response to which problems.

In one example, a location of a potential bottleneck of network traffic in a network is identified. Based on the location of the potential bottleneck, a first QoE inference model is selected from a plurality of respective QoE inference models. The respective QoE inference models are each trained to infer a respective QoE of the network traffic based on one or more respective network traffic metrics generated by monitoring the network traffic at a respective location in the network. One or more first network traffic metrics of the one or more respective network traffic metrics are generated by monitoring the network traffic at a first respective location. The one or more first network traffic metrics are provided to the first QoE inference model to infer a first respective QoE.

A method includes: receiving a current data packet of a target service; adding detection indication information to the current data packet, to obtain a target data packet, where the detection indication information includes a target phase category corresponding to the current data packet, the target phase category is used to indicate a target phase in at least one key phase in which the current data packet is located in the target service, and the detection indication information is used to indicate to detect the current data packet; and transmitting the target data packet to a next hop node.

A method includes: receiving a current data packet of a target service; adding detection indication information to the current data packet, to obtain a target data packet, where the detection indication information includes a target phase category corresponding to the current data packet, the target phase category is used to indicate a target phase in at least one key phase in which the current data packet is located in the target service, and the detection indication information is used to indicate to detect the current data packet; and transmitting the target data packet to a next hop node.

Techniques are described in which a network management system (NMS) is configured to determine a root cause of degraded network performance based on SLE metrics and the locations associated with network devices providing the SLE metrics. The NMS can determine service level experience (SLE) metrics associated with each client device on a network and location data for each client device of the plurality of client devices. The NMS can generate a time series of parameter vectors, where each parameter vector includes SLE metrics corresponding to each client device of the plurality of client devices. Each parameter vector is associated with the location of the client device corresponding to the SLE metrics. The NMS can determine, based on the time series of parameter vectors and associated locations, a root cause for a degradation in SLE metrics associated with the one or more of the client devices.

The disclosure relates to a 5th generation (5G) or pre-5G communication system for supporting a higher data transmission rate than that of a beyond-4th generation (4G) communication system such as long-term evolution (LTE). The disclosure provides a method performed by a terminal, the method including: transmitting, to a base station, capability information including information indicating whether the terminal supports UL RRC segmentation; and transmitting, to the base station, a message regarding a QoE report, wherein, when the terminal supports the UL RRC segmentation, the message regarding the QoE report is segmented based on a size of a PDCP SDU.

The disclosure relates to a 5th generation (5G) or pre-5G communication system for supporting a higher data transmission rate than that of a beyond-4th generation (4G) communication system such as long-term evolution (LTE). The disclosure provides a method performed by a terminal, the method including: transmitting, to a base station, capability information including information indicating whether the terminal supports UL RRC segmentation; and transmitting, to the base station, a message regarding a QoE report, wherein, when the terminal supports the UL RRC segmentation, the message regarding the QoE report is segmented based on a size of a PDCP SDU.

An agent interfaces with a sending computing device and a receiving computing device to automatically adjust one-way or two-way real-time audio and real-time video transmission parameters responsive to changing network conditions and/or application requirements. The agent incorporates a reinforcement learning model that adjusts transmission parameters to maximize an expected value of a sum of future rewards; the expected value of the sum of future rewards is based on a current state of the sending computing, a current action (e.g. a current set of transmission parameters) at the sending computing device and a reward provided by the receiving computing device. The reward is representative of a user-perceived quality of experience at the receiving computing device.