Abstract:
Aspects of the subject disclosure may include, for example, receiving, from a machine learning model, information about an event causing a service degradation in a cellular network, wherein the event is external to the cellular network, determining one or more event categories associated with the event causing the service degradation, determining, based on the one or more event categories, likely affected customers, the likely affected customers being likely to experience the service degradation, determining, by the machine learning model, proper resources for resolution of the service degradation, wherein the determining proper resources is based on the one or more event categories, and dispatching the proper resources for resolution of the service degradation. Other embodiments are disclosed.
Abstract:
Aspects of the subject disclosure may include, for example, allocating, by a processing system including a processor, a first subset of resources to a first plurality of applications and a second subset of the resources to a second plurality of applications, wherein the allocating is based on respective statuses associated with the first plurality of applications and the second plurality of applications, and assigning, by the processing system, a respective bitrate to each application of the first plurality of applications, wherein the assigning of the respective bitrate to each application of the first plurality of applications is based on: a first threshold associated with a re-buffering of content, and a second threshold associated with the statuses. Other embodiments are disclosed.
Abstract:
Aspects of the subject disclosure may include, for example, allocating, by a processing system including a processor, a first subset of resources to a first plurality of applications and a second subset of the resources to a second plurality of applications, wherein the allocating is based on respective statuses associated with the first plurality of applications and the second plurality of applications, and assigning, by the processing system, a respective bitrate to each application of the first plurality of applications, wherein the assigning of the respective bitrate to each application of the first plurality of applications is based on: a first threshold associated with a re-buffering of content, and a second threshold associated with the statuses. Other embodiments are disclosed.
Abstract:
A mechanism is provided for predicting video engagement from network measurements for a user device connected to a wireless network. Wireless network measurements are retrieved from a wireless network device in the wireless network. The wireless network measurements are related to the user device of a user. It is determined that the user device is engaged in a video streaming session. A computer classifies the video streaming session as one of a plurality of classes, in which the plurality of classes predict an outcome of the video streaming session for the user device.
Abstract:
A system and techniques are disclosed that increase the redundancy (i.e., physical diversity and bandwidth) available to an IP network, thereby increasing the failure processing capability of IP networks. The techniques include pooling the resources of multiple networks together for mutual backup purposes to improve network reliability and employing methods to efficiently utilize both the intradomain and the interdomain redundancies provided by networks at low cost.