Abstract: Estimation of network latency based on aggregated performance data is disclosed. End-to-end latency between endpoints can comprise protocol-induced latency, access network-induced, core-network latency, and network-distance-based latency. Network latency, e.g., network-distance-based latency, also referred to as topological latency, can correspond to communication path length between endpoints. Crowdsourcing of communication path performance information can enable estimation of topological latency, and derivatives thereof, such as topological distance. Crowdsourcing can be the practice of obtaining information or input into a task or project by enlisting the services of a large number of people, either paid or unpaid, e.g., collection of performance information.

Abstract: Server selection for improving latency with an instance of a service is disclosed. Generally, lower core latency is preferrable. Core latencies can be related to a physical distance between endpoints connected via a communication pathway, such as a first server and second server connected via network equipment. A geographic distance between endpoints can be different from a topological distance of a communication pathway between endpoints. Endpoints, e.g., servers, can be ranked based on topological distance. The ranking can be communicated to service provider equipment to enable selection of a server based on a corresponding ranking of the server. Selection of an endpoint based on topological distance can enable improved latency in comparison to selecting an endpoint based on geographical distance. Improved latency can result in an improved quality of experience for a user of a service supported by an endpoint selected based on topological distance.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for locally managing quality of experience in a home network. For instance, in one example, a method performed by a residential gateway connected to a home network includes capturing network data relating to a plurality of software applications executing on a plurality of user endpoint devices connected to the home network, converting the network data into at least one network condition metric, receiving a query from a first software application of the plurality of software applications for at least one current condition of the home network, and delivering the at least one network condition metric to the first software application.

Abstract: Aspects of the subject disclosure may include, for example, detecting a loss of at least a part of a packet conveyed via a wireless link coupling a processing system and a user equipment, based on the detecting, identifying a cause of the loss as being due to one of a signal strength of a signal being less than a first threshold or an amount of data in a buffer exceeding a second threshold, resulting in an identification, and based on the identification, invoking an action. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining data pertaining to a communication service facilitated at least in part by a first gateway located in a building, the communication service involving at least a first user equipment, analyzing the data to determine that a processing load supported by the first gateway exceeds a first threshold, wherein the first threshold is expressed as a function of a capacity of the first gateway, resulting in a determination, and based on the determination, performing an action that causes the processing load supported by the first gateway to be reduced to a level that is less than the first threshold. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining, over a communication network, flow records from each of a group of smart residential gateways (SRGs) resulting in a group of flow records. Each of the group of SRGs is associated with one or more communication devices resulting in a group of communication devices. Further embodiments include determining a first Quality of Experience (QoE) metric for a first portion of the group of communication devices based on the group of flow records, and determining that the first QoE metric is below a first QoE metric threshold. Additional embodiments include identifying a first portion of the group of SRGs associated with the portion of the group of communication devices, and allocating a first group of network resources to a first portion of the communication network associated with the first portion of the group of SRGs. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method including facilitating communications between a wide area network (WAN) and a local area network (LAN), executing an application mirror, associated with a media server coupled to the WAN, via an operating system at the residential gateway device, prefetching, via the application mirror, a plurality of prefetched media content from the media server according to a client application associated with the media server and operating at user device coupled to the LAN, storing, via the application mirror, the plurality of prefetched media content, determining, via the application mirror, that the plurality of prefetched media content includes a first media content, and responsive to the determining the plurality of prefetched media content includes the first media content, transmitting, via the application mirror, the first media content to the client application at the user device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a processing system that performs operations including collecting encrypted network traffic flow data from user interaction with an application, deriving a first set of traffic feature vectors from the encrypted network traffic flow data collected, training a machine learning algorithm on the first set of traffic feature vectors to classify each traffic feature vector in the first set of traffic feature vectors as associated with a type of the application or not associated with the type of the application, and classifying whether an encrypted network traffic flow as the type of the application by applying the machine learning algorithm to a traffic feature vector of the encrypted network traffic flow. Other embodiments are disclosed.

Abstract: In one example, a processing system including at least one processor may obtain a predicted viewport of a mobile computing device for an immersive visual stream, identify a first plurality of blocks of a frame of the immersive visual stream that are associated with the predicted viewport, encode the first plurality of blocks at a first encoding quality level, and encode a second plurality of blocks of the frame at a second encoding quality level, where the second encoding quality level is associated with a lesser visual quality as compared to the first encoding quality level and where the second plurality of blocks are outside of the predicted viewport. The processing system may then transmit the frame having the first plurality of blocks encoded at the first encoding quality level and the second plurality of blocks encoded at the second encoding quality level to the mobile computing device.

Abstract: Aspects of the subject disclosure may include, for example, receiving a first plurality of operating parameters associated with a first plurality of communication devices, obtaining a first prediction of a performance of a first communication device included in the first plurality of communication devices that is based on an application of the first plurality of operating parameters to a model, and responsive to a determination that the first prediction of the performance is inadequate in accordance with a first threshold, transmitting a first command to at least one communication device included in the first plurality of communication devices to modify at least one operating parameter of the at least one communication device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include obtaining video content, the video content comprises a plurality of frames, monitoring, by an image sensor, a facial feature of a user to determine a visual focus of the user in relation to the video content, and detecting from a group of frames of the plurality of frames at least a reduction in movements of objects in the group of frames. Further embodiments include determining, according to the monitoring and the detecting, a measure of attention of the user within a region of the group of frames, determining that the measure of attention of the user within the region of the group of frames satisfies a threshold, and embedding in at least a portion of subsequent frames of the plurality of frames a visual advertisement in the region via a communication device. Other embodiments are disclosed.

Abstract: A method includes obtaining a first plurality of encrypted traffic flows traversing a communication network, performing a first classification, wherein a result of the first classification identifies a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, and wherein the first classification is based on a traffic pattern of the each encrypted traffic flow, performing a second classification, wherein a result of the second classification identifies a traffic type associated with each server name indication from which the first plurality of encrypted traffic flows is associated, and wherein the second classification is based on the result of the first classification, and performing a third classification identifying a traffic type associated with each encrypted traffic flow of the first plurality of encrypted traffic flows, wherein the third classification is based on a combination of the results of the first classification and the second classification.

Abstract: Aspects of the subject disclosure may include, for example, obtaining one or more signals, the one or more signals being based upon brain activity of a viewer while the viewer is viewing media content; predicting, based upon the one or more signals, a first predicted desired viewport of the viewer; obtaining head movement data associated with the media content; predicting, based upon the head movement data, a second predicted desired viewport of the viewer; comparing the first predicted desired viewport to the second predicted desired viewport, resulting in a comparison; and determining, based upon the comparison, to use the first predicted desired viewport to facilitate obtaining a first subsequent portion of the media content or to use the second predicted desired viewport to facilitate obtaining a second subsequent portion of the media content. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method, including identifying recommended video clips for a user of a communication device according to a video viewing profile and video subject matter information associated with a plurality of video clips, identifying an edge cloud server for facilitating network access by the communication device at a location, determining a reduced activity period for a data path between a video content server and the edge cloud server by comparing activity information for the data path and an activity threshold, directing the video content server to store the recommended video clips at the edge cloud server during the reduced activity period, and providing a listing of the recommended video clips to an application, where the communication device receives a video clip from the edge cloud server responsive to a selection of the video clip via the application. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, selecting a set of traffic counters, wherein the traffic counters provide a profile of viewing habits of a user, and wherein the traffic counters are extracted from video streaming by the user; predicting a size of a video buffer based on the traffic counters selected; and building the video buffer based on the predicted size. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include obtaining video content, the video content comprises a plurality of frames, monitoring, by an image sensor, a facial feature of a user to determine a visual focus of the user in relation to the video content, and detecting from a group of frames of the plurality of frames at least a reduction in movements of objects in the group of frames. Further embodiments include determining, according to the monitoring and the detecting, a measure of attention of the user within a region of the group of frames, determining that the measure of attention of the user within the region of the group of frames satisfies a threshold, and embedding in at least a portion of subsequent frames of the plurality of frames a visual advertisement in the region via a communication device. Other embodiments are disclosed.

Abstract: In one example, a processing system including at least one processor may obtain a predicted viewport of a mobile computing device for an immersive visual stream, identify a first plurality of blocks of a frame of the immersive visual stream that are associated with the predicted viewport, encode the first plurality of blocks at a first encoding quality level, and encode a second plurality of blocks of the frame at a second encoding quality level, where the second encoding quality level is associated with a lesser visual quality as compared to the first encoding quality level and where the second plurality of blocks are outside of the predicted viewport. The processing system may then transmit the frame having the first plurality of blocks encoded at the first encoding quality level and the second plurality of blocks encoded at the second encoding quality level to the mobile computing device.

Abstract: Aspects of the subject disclosure may include, for example, obtaining one or more signals, the one or more signals being based upon brain activity of a viewer while the viewer is viewing media content; predicting, based upon the one or more signals, a first predicted desired viewport of the viewer; obtaining head movement data associated with the media content; predicting, based upon the head movement data, a second predicted desired viewport of the viewer; comparing the first predicted desired viewport to the second predicted desired viewport, resulting in a comparison; and determining, based upon the comparison, to use the first predicted desired viewport to facilitate obtaining a first subsequent portion of the media content or to use the second predicted desired viewport to facilitate obtaining a second subsequent portion of the media content. Other embodiments are disclosed.

Abstract: The described technology is generally directed towards reducing latency in a wireless communications network. Radio access network latency data corresponding to a measured latency impact criterion is obtained by a network device of a wireless network. Based on the radio access network latency data, latency guidance data usable by the radio network device to achieve a reduction in communication latency that is experienced by a user equipment is predicted, e.g., by a learned model. The latency guidance data can be used to facilitate a reduction in the communication latency that is experienced by a user equipment.

Abstract: Aspects of the subject disclosure may include, for example, a method, including identifying recommended video clips for a user of a communication device according to a video viewing profile for the user and video subject matter information associated with a plurality of video clips, identifying an edge cloud server for facilitating network access by the communication device at a location, determining a reduced activity period for a data path between a video content server and the edge cloud server by comparing activity information for the data path and an activity threshold, directing the video content server to store the recommended video clips at the edge cloud server during the reduced activity period, and providing a listing of the recommended video clips to an application, where the communication device receives a video clip of the recommended video clips from the edge cloud server responsive to a selection of the video clip via the application. Other embodiments are disclosed.