Abstract: Aspects of the subject disclosure may include, for example, setting a streaming rate cap for a client device; obtaining a bitrate ladder associated with the client device, wherein the bitrate ladder comprises a listing of a plurality of bitrates that the client device can request in connection with receiving streaming data; determining a boost rate for the client device, wherein the boost rate is greater than the streaming rate cap such that the boost rate enables the client device to receive the streaming data faster than the client device would otherwise receive the streaming data under the streaming rate cap, and wherein the boost rate is based upon the bitrate ladder; and enabling delivery to the client device of the streaming data up to the boost rate; wherein a network device is part of a network through which the streaming data is delivered to the client device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, setting a streaming rate cap for a client device; obtaining a bitrate ladder associated with the client device, wherein the bitrate ladder comprises a listing of a plurality of bitrates that the client device can request in connection with receiving streaming data; determining a boost rate for the client device, wherein the boost rate is greater than the streaming rate cap such that the boost rate enables the client device to receive the streaming data faster than the client device would otherwise receive the streaming data under the streaming rate cap, and wherein the boost rate is based upon the bitrate ladder; and enabling delivery to the client device of the streaming data up to the boost rate; wherein a network device is part of a network through which the streaming data is delivered to the client device. Other embodiments are disclosed.

Abstract: A buffer classification system calculates first flow metrics for a first flow in response to receiving first packet level data. The system in response to receiving HTTP information, generates buffer emulation data and creates one or more data training sets using the first flow metrics and buffer emulation data, wherein training data used to create the one or more training data sets is further generated by computing an average throughput per second observed over different time windows during a video playback. The system in response to creating the one or more training data sets, generates one or more classifier rules used to determine a buffer condition of a flow. The system in response to receiving second packet level data, calculates second flow metrics for a second flow and predicts a buffer condition for the second flow based on the second flow metrics and the one or more classifier rules.

Abstract: A buffer classification system comprising: a buffer emulator; a metrics calculator; a learning module; a training module; a classification module; wherein the buffer emulator is connected to a trusted proxy on a network, the trusted proxy in communication with a client server; the buffer emulator and metrics calculator are connected to the trusted proxy; the buffer emulator emulating a buffer condition of the trusted proxy; the metrics calculator is configured obtain at least one measurement from a flow between the trusted proxy and a client; wherein the learning module is configured to map the buffer condition to the at least one measurement, and communicates a mapped metric to the training module; wherein the training module is configured to define at least one classifier rules to predict a buffer condition from a calculated metric; and wherein the training module transmits the at least one classifier rule to a classifier module, the classifier module having an input configured to receive a user flow, the c