Abstract: Aspects of the subject disclosure may include, for example, adjusting bandwidth allocated for first communication devices of a first communication network from a first bandwidth limit to no bandwidth limit, measuring first metrics of the first communication devices, computing second metrics of second communication devices of a second communication network, in which there is no bandwidth limit allocated to the second communication devices, determining a first allocation of first network resources for the second communication devices.

Abstract: Aspects of the subject disclosure may include, for example, adjusting bandwidth allocated for a first group of communication devices communicatively coupled to a first communication network from a bandwidth limited allocation to a no bandwidth limited allocation, and measuring a first group of metrics associated with the first group of communication devices associated with the first communication network. Further embodiments include computing a second group of metrics associated with a second group of communication devices communicatively coupled to a second communication network according to the first group of metrics, the second group of communication devices have the no bandwidth limited allocation, and allocating a first group of network resources for the second group of communication devices according to the second group of metrics. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying a flow of data packets between first and second network addresses of a network, with each packet including respective header and payload portions. The identified flow of data packets is monitored over a number of sample periods to obtain a number of monitored results. A data-flow activity record is generated, having a number of symbols corresponding to the number of monitored results, the symbols including an active symbol value indicative of a presence of an exchange of data and an idle symbol value indicative of an absence of an exchange of data. A suitability of the identified data flow is inferred for estimating a throughput of the network according to the data-flow activity record without interpreting contents of each respective packet payload portion. Other embodiments are disclosed.

Abstract: A processing system including at least one processor may identify an excess utilization of a telecommunication network by a plurality of mobile monitoring devices. The plurality of mobile monitoring devices may be of a same mobile monitoring device type. In addition, the plurality of mobile monitoring devices may be associated with a plurality of users and wherein a primary function of the plurality of mobile monitoring devices is inaccessible to control by the plurality of users. The processing system may then determine that the excess utilization is not caused by the primary function of the plurality of mobile monitoring devices and perform at least one remedial action in response to the detecting of the excess utilization that is not caused by the primary function of the plurality of mobile monitoring devices.

Abstract: Aspects of the subject disclosure may include, for example, a device that has 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 downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: Network capacity planning based on application performance can include detecting a data session occurring on a network, identifying an application being used for the data session, where the application can include a video application, determining if a performance model for the video application exists, the performance model describing performance metrics and quality of service events associated with the video application, determining, based on the performance model, a capacity planning trigger for the video application, where the capacity planning trigger can include increasing network capacity based on the needs and a quality of service associated with the video application during the data session, and generating a command that, when executed by a network entity, causes the network entity to implement the capacity planning trigger on the network.

Abstract: Aspects of the subject disclosure may include, for example, a device, that includes 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 receiving a manifest for a point cloud, wherein the point cloud is partitioned into a plurality of cells; determining an occlusion level for a cell of the plurality of cells with respect to a predicted viewport; reducing a point density for the cell provided in the manifest based on the occlusion level, thereby determining a reduced point density; and requesting delivery of points in the cell, based on the reduced point density. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device that has 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 downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, embodiments for obtaining video content from a video content server, determining a quality parameter for each of the plurality of adjusted video content, and adjusting the video content to generate the plurality of adjusted video content. Each of the plurality of adjusted video content comprises a different point cloud density according to the quality parameter. Further embodiments can include receiving a request for the video content from a video content application on a communication device, and determining a distance from a viewpoint to a point-cloud object on the video content application of the communication device. Additional embodiments can include selecting an adjusted video content based on the distance from the viewpoint to the point-cloud object and the quality parameter of the adjusted video content, and providing the adjusted video content to the communication device for presentation. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method performed by a processing system of determining a present orientation of a display region presented at a first time on a display of a video viewer, predicting a future orientation of the display region occurring at a second time based on data collected, to obtain a predicted orientation of the display region to be presented at the second time on the display of the video viewer, identifying, based on the predicted orientation of the display region, a first group of tiles from a video frame of a panoramic video being displayed by the video viewer, wherein the first group of tiles covers the display region in the video frame at the predicted orientation, and a plurality of objects moving in the video frame from the first time to the second time, wherein each object of the plurality of objects is located in a separate spatial region of the video frame at the second time, wherein a second group of tiles collectively covers the separate spatial re

Abstract: Aspects of the subject disclosure may include, for example, a device, that includes 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 receiving a manifest for a point cloud, wherein the point cloud is partitioned into a plurality of cells; determining an occlusion level for a cell of the plurality of cells with respect to a predicted viewport; reducing a point density for the cell provided in the manifest based on the occlusion level, thereby determining a reduced point density; and requesting delivery of points in the cell, based on the reduced point density. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising predicting a viewpoint within a volumetric video based on a movement input provided by a viewer, resulting in a predicted viewpoint at a future time, retrieving a cell occupancy bitmap for a point cloud of the volumetric video for the future time, determining visible cells based on the cell occupancy bitmap and the predicted viewpoint, where the visible cells are not obscured by points in other cells, and retrieving points of the point cloud that are within the visible cells prior to the future time.

Abstract: Aspects of the subject disclosure may include, for example, a device, that includes 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 receiving a manifest for a point cloud, wherein the point cloud is partitioned into a plurality of cells; determining an occlusion level for a cell of the plurality of cells with respect to a predicted viewport; reducing a point density for the cell provided in the manifest based on the occlusion level, thereby determining a reduced point density; and requesting delivery of points in the cell, based on the reduced point density. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising predicting a viewpoint within a volumetric video based on a movement input provided by a viewer, resulting in a predicted viewpoint at a future time, retrieving a cell occupancy bitmap for a point cloud of the volumetric video for the future time, determining visible cells based on the cell occupancy bitmap and the predicted viewpoint, where the visible cells are not obscured by points in other cells, and retrieving points of the point cloud that are within the visible cells prior to the future time.

Abstract: Aspects of the subject disclosure may include, for example, a device that has 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 downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device that has 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 downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, embodiments for obtaining video content from a video content server, determining a quality parameter for each of the plurality of adjusted video content, and adjusting the video content to generate the plurality of adjusted video content. Each of the plurality of adjusted video content comprises a different point cloud density according to the quality parameter. Further embodiments can include receiving a request for the video content from a video content application on a communication device, and determining a distance from a viewpoint to a point-cloud object on the video content application of the communication device. Additional embodiments can include selecting an adjusted video content based on the distance from the viewpoint to the point-cloud object and the quality parameter of the adjusted video content, and providing the adjusted video content to the communication device for presentation. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device that has 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 selecting a downsampling technique for removing points from a point cloud in a volumetric video; selecting a data reduction percentage for a target bitrate for streaming the volumetric video; downsampling the point cloud in each frame of the volumetric video for the target bitrate using the downsampling technique and data reduction percentage selected, thereby creating a downsampled volumetric video; and compressing and encoding the downsampled volumetric video, thereby creating an encoded volumetric video. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, encoding a point cloud in a first frame of volumetric video in an octree format and identifying a first group of points of the point cloud, resulting in a first base layer including a base layer, a first enhancement layer and a second enhancement layer. Each layer includes different points from the point cloud, with the second enhancement layer including all points in each octet of leaf nodes in the octree format of the point cloud that are not in the base layer and the first enhancement layer. The first base layer of the point cloud is provided over a communication network to a media device and the first enhancement layer is also provided responsive to a determination of sufficient available bandwidth in the communication network. Other embodiments are disclosed.

Abstract: In one example, a processing system of a mobile computing device including at least one processor may capture visual information of a first location, transmit a recognition request to a first server, the recognition request comprising the visual information, obtain a first recognition result from the first server comprising first annotation content associated with a first item in the visual information of the first location, the first recognition result further comprising visual information of the first item, present at least a portion of first annotation content via the mobile computing device, store the first annotation content and the visual information of the first item in a local cache, detect a first additional mobile computing device via a non-cellular wireless communication modality, and share the first annotation content and the visual information of the first item with the first additional mobile computing device via the non-cellular wireless communication modality.