Abstract: Aspects of the subject disclosure may include, for example, a method comprising: obtaining media content, such as video; receiving a request from a user and/or user equipment (UE) to view the media content; identifying a predicted field of view of the user; sending the predicted field of view to the equipment of the user at a normal priority; monitoring a line of sight (or actual field of view) of the user; determining a field of view error as a difference between the line of sight and the predicted field of view; and sending the field of view error to the equipment of the user at a higher priority. Other embodiments are disclosed.

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, 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: 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, a method comprising: obtaining media content, such as video; receiving a request from a user and/or user equipment (UE) to view the media content; identifying a predicted field of view of the user; sending the predicted field of view to the equipment of the user at a normal priority; monitoring a line of sight (or actual field of view) of the user; determining a field of view error as a difference between the line of sight and the predicted field of view; and sending the field of view error to the equipment of the user at a higher priority. Other embodiments are disclosed.

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: Concepts and technologies disclosed herein are directed to collective intelligence-based cell congestion detection in mobile telecommunications networks. According to some aspects of the concepts and technologies disclosed herein, a core network element or device application can detect cell congestion based upon Internet layer, transport layer, and application layer data, such as, for example, traffic type, volume, rate, latency, jitter, and the like. According to one aspect disclosed herein, a cell congestion detection (“CCD”) system can collect data from an Internet layer, a transport layer, and an application layer. The CCD system can apply a machine learning algorithm to the data to determine whether the cell is congested. According to another aspect the CCD system can collect the data that is associated with a specific application executed by a plurality of UE devices connected to a cell.

Abstract: In one example, the encoding rate at which data is delivered over a network is estimated by intercepting a data packet from a data streaming session, wherein the data packet contains at least a portion of one of a plurality of file segments containing portions of a file, and wherein a number of the plurality of file segments that is delivered via the data streaming session is limited to a maximum number. The mean size of the plurality of file segments is estimated from data contained in the data packet. A plurality of potential sizes of the plurality of file segments is calculated, wherein each potential size corresponds to one number between one and the maximum number. An encoding rate of the data streaming session is calculated, based at least in part on which potential size of the plurality of potential sizes is closest to the mean size.

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: In one example, a processing system including at least one processor may obtain viewport information of a mobile computing device associated with a panoramic video, map the viewport information to a plurality of blocks of a frame of the panoramic video in accordance with a cube map projection of the frame, encode the plurality of blocks into a projected frame, and transmit the projected frame to the mobile computing device. In another example, a processing system of a mobile computing device including at least one processor may obtain viewport information of the device associated with a panoramic video, transmit to a server the viewport information, obtain from the server a projected frame of the panoramic video comprising a plurality of blocks of a frame of the panoramic video that are associated with the viewport information in accordance with a cube map projection of the frame, and buffer the projected frame.

Abstract: Aspects of the subject disclosure may include, for example, a system that provides for obtaining network parameter data from an eNodeB and a content delivery network server of a wireless network, determining a predicted network condition for a group of end user devices to receive an over-the-top video service in a coverage area of the eNodeB according to the network parameter data, and providing access to prediction information representative of the predicted network condition. The providing the access to the prediction information representative of the predicted network condition enables a video client of an end user device in the coverage area to provide a request for a video chunk to a video server according to buffer information and the predicted network condition Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device in which a processing system enables mobile devices on a communication network to perform latency tests to obtain first latency test data, determines an edge latency for each mobile device resulting in edge latencies, identifies a group of mobile device clusters according to the edge latencies, and identifies network edge server locations according to the group of mobile device clusters. The processing system also receives second latency test data from each of the network edge servers, and determines a data center latency for each of the network edge servers, resulting in a plurality of data center latencies. The processing system also identifies a data center location according to the data center latencies, and determines a network topology for the communication network; the network topology includes the data center location and the edge server locations. 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, 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.

Abstract: Aspects of the subject disclosure may include, for example, applying a model to a first unlabeled traffic item to generate a classification of the first unlabeled traffic item as a first class of traffic items or a second class of traffic items, generating a first score that is representative of a first confidence of the classification, determining that the first score is greater than a first threshold, and responsive to determining that the first score is greater than the first threshold, refining the model in accordance with at least the classification, resulting in a modified model. Other embodiments are disclosed.

Abstract: Devices, computer-readable media, and methods for selecting a type of packet loss protection for a network-based communication based upon a latency estimate are disclosed. For example, a processing system including at least one processor may obtain a latency estimate for a network-based communication, determine whether the latency estimate exceeds a latency threshold for selecting a type of packet loss protection, and select, the type of packet loss protection for the network-based communication from among a first type of packet loss protection and a second type of packet loss protection based upon the determining. When the latency estimate is determined to not exceed the latency threshold, the first type of packet loss protection is selected. When the latency estimate is determined to exceed the latency threshold, the second type of packet loss protection is selected.

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 device including a processing system including a processor and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including monitoring network traffic to determine a transmission control protocol traffic pattern, determining a target video rate from the transmission control protocol traffic pattern, and modifying an over-the-top video delivery service network according to the target video rate. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device in which a processing system enables mobile devices on a communication network to perform latency tests to obtain first latency test data, determines an edge latency for each mobile device resulting in edge latencies, identifies a group of mobile device clusters according to the edge latencies, and identifies network edge server locations according to the group of mobile device clusters. The processing system also receives second latency test data from each of the network edge servers, and determines a data center latency for each of the network edge servers, resulting in a plurality of data center latencies. The processing system also identifies a data center location according to the data center latencies, and determines a network topology for the communication network; the network topology includes the data center location and the edge server locations. Other embodiments are disclosed.