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, 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: Optimal data packet size is identified to achieve a higher throughput a wireless communication network. For example, a system can comprise monitoring a transmit control protocol performance associated with a first transmission of data packets over a first duration of time, wherein a packet size of the data packets is a first data packet size, detecting that the transmit control protocol performance satisfies a function with respect to a threshold and in response to the detecting that transmit control protocol performance satisfies the function with respect to the first threshold, determining a second data packet size to use for a second transmission of the data packets over a second duration of time, transmitting a request to change the packet size of the data packets to the second data packet.

Abstract: Aspects of the subject disclosure may include, for example, a method including monitoring a video session to determine key performance indicators for cross-layer interactions between a network providing video session video data and user equipment receiving the video session video data, wherein the key performance indicators include a transmission control protocol congestion window size that corresponds to a video chunk requested by the user equipment, and a radio access network throughput of a download of the video chunk requested by the user equipment, and determining a quality of service for the user equipment during the video session according to a residual length of content in a playback buffer for the user equipment based on the transmission control protocol congestion window size, and the radio access network throughput. 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 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, 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 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: Various embodiments disclosed herein provide for identifying optimal data packet size to achieve a higher throughput a wireless communication network. According to some embodiments, a system can comprise monitoring a transmit control protocol performance associated with a first transmission of data packets over a first duration of time, wherein a packet size of the data packets is a first data packet size, detecting that the transmit control protocol performance satisfies a function with respect to a threshold and in response to the detecting that transmit control protocol performance satisfies the function with respect to the first threshold, determining a second data packet size to use for a second transmission of the data packets over a second duration of time, transmitting a request to change the packet size of the data packets to the second data packet.

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, 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: Aspects of the subject disclosure may include, for example, a machine-readable storage medium, including executable instructions that, when executed by a processing system including a processor, facilitate performance of operations that include assuming control of a video content transfer between a video server and a video client for HTTP traffic responsive to determining the HTTP traffic is for video content, generating a video transfer rate selection and a video transfer resolution selection for a requested video chunk of a time slot of the video content, generating an optimized manifest file for the requested video chunk, and publishing the optimized manifest file to the video client, wherein the video client request the requested video chunk from the video server based on the optimized manifest. 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: 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, the present disclosure describes a device, computer-readable medium, and method for estimating the streaming rate at which data is delivered over a network. For instance, in one example, a data packet is intercepted from a data streaming session conducted between a first device and a second device connected over a network. A size of a file segment contained in the data packet is calculated. The file segment corresponds to a portion of an item of multimedia content being delivered via the data streaming session. The encoding rate of the data streaming session is then calculated based at least in part on the size of the file segment.

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, a machine-readable storage medium, including executable instructions that, when executed by a processing system including a processor, facilitate performance of operations that include assuming control of a video content transfer between a video server and a video client for HTTP traffic responsive to determining the HTTP traffic is for video content, generating a video transfer rate selection and a video transfer resolution selection for a requested video chunk of a time slot of the video content, generating an optimized manifest file for the requested video chunk, and publishing the optimized manifest file to the video client, wherein the video client request the requested video chunk from the video server based on the optimized manifest. Other embodiments are disclosed.

Abstract: A processing system including at least one processor may generate a binary traffic vector from a traffic flow in a communication network. The binary traffic vector may comprise a plurality of elements, each associated with a respective time period and comprising one of: a first value or a second value. For each time period the traffic flow comprises a transfer of a data unit, a corresponding element comprises the first value, and for time period the traffic flow does not comprise a transfer of a data unit, a corresponding element comprises the second value. The processing system may then apply a traffic flow record comprising the binary traffic vector as an input to a deep learning classifier trained to classify traffic flow records into traffic categories, and determine a traffic category from an output of the deep learning classifier in accordance with the traffic flow record as the input.

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.