Video transport stream stability prediction

1 . A computer-implemented method for training a machine-learning model to perform a video stability prediction for a video stream, the computer-implemented method comprising:

receiving, by one or more processors, a plurality of data packets carrying data of a video stream from a video source;

obtaining, by the one or more processors, a set of network performance metrics corresponding to at least one of the plurality of data packets;

determining, by the one or more processors, a source or a destination associated with the at least one of the plurality of data packets;

collecting, by the one or more processors, a data time series of data based on the source or the destination; and

training, by the one or more processors, one or more machine-learning models based on the time series of data and the source or the destination to generate a video stability score.

2 . The computer-implemented method of claim 1 , wherein the set of network performance metrics include at least one of: a latency metric, a packet loss metric, a jitter metric, or a bandwidth utilization metric.

3 . The computer-implemented method of claim 1 , wherein obtaining the set of network performance metrics includes:

retrieving, by the one or more processors, one or more priority fault errors from a packet header of at least one of the plurality of data packets.

4 . The computer-implemented method of claim 1 , wherein collecting the time series of data includes:

analyzing, by the one or more processors, the source or the destination of the at least one of the plurality of data packets to determine a location reference associated with the source or the destination; and

correlating, by the one or more processors, the location reference with location data.

5 . The computer-implemented method of claim 4 , wherein the location data includes at least one of: a weather pattern, a network condition, a media property, or a bandwidth allocation.

6 . The computer-implemented method of claim 4 , wherein training the one or more machine-learning models based on the time series of data and the source or the destination to generate the video stability score includes:

training, by the one or more processors, the one or more machine-learning models based on the location data.

7 . The computer-implemented method of claim 1 , wherein the video stability score includes a failure prediction or a stability prediction for the video stream.

8 . The computer-implemented method of claim 1 , the computer-implemented method further comprising:

validating, by the one or more processors, the one or more trained machine-learning models to determine an accuracy for each of the one or more trained machine-learning models; and

selecting, by the one or more processors, at least one of the one or more trained machine-learning models with a highest accuracy for utilization for generating a future video stability prediction.

9 . A computer system for training a machine-learning model to perform a video stability prediction for a video stream, the computer system comprising:

a memory having processor-readable instructions stored therein; and

one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions, including functions for:

receiving, by the one or more processors, a plurality of data packets carrying data of a video stream from a video source;

obtaining, by the one or more processors, a set of network performance metrics corresponding to at least one of the plurality of data packets;

determining, by the one or more processors, a source or a destination associated with the at least one of the plurality of data packets;

collecting, by the one or more processors, a data time series of data based on the source or the destination; and

training, by the one or more processors, one or more machine-learning models based on the time series of data and the source or the destination to generate a video stability score.

10 . The computer system of claim 9 , wherein the set of network performance metrics include at least one of: a latency metric, a packet loss metric, a jitter metric, or a bandwidth utilization metric.

11 . The computer system of claim 9 , wherein obtaining the set of network performance metrics includes:

retrieving, by the one or more processors, one or more priority fault errors from a packet header of at least one of the plurality of data packets.

12 . The computer system of claim 9 , wherein collecting the time series of data includes:

analyzing, by the one or more processors, the source or the destination of the at least one of the plurality of data packets to determine a location reference associated with the source or the destination; and

correlating, by the one or more processors, the location reference with location data.

13 . The computer system of claim 12 , wherein the location data includes at least one of: a weather pattern, a network condition, a media property, or a bandwidth allocation.

14 . The computer system of claim 12 , wherein training the one or more machine-learning models based on the time series of data and the source or the destination to generate the video stability score includes:

training, by the one or more processors, the one or more machine-learning models based on the location data.

15 . The computer system of claim 9 , wherein the video stability score includes a failure prediction or a stability prediction for the video stream.

16 . The computer system of claim 9 , the functions further comprising:

validating, by the one or more processors, the one or more trained machine-learning models to determine an accuracy for each of the one or more trained machine-learning models; and

selecting, by the one or more processors, at least one of the one or more trained machine-learning models with a highest accuracy for utilization for generating a video stability prediction.

17 . A non-transitory computer-readable medium containing instructions for training a machine-learning model to perform a video stability prediction for a video stream the instructions comprising:

receiving a plurality of data packets carrying data of a video stream from a video source;

obtaining a set of network performance metrics corresponding to at least one of the plurality of data packets;

determining a source or a destination associated with the at least one of the plurality of data packets;

collecting a data time series of data based on the source or the destination; and

training one or more machine-learning models based on the time series of data and the source or the destination to generate a video stability score.

18 . The non-transitory computer-readable medium of claim 17 , wherein the set of network performance metrics include at least one of: a latency metric, a packet loss metric, a jitter metric, or a bandwidth utilization metric.

19 . The non-transitory computer-readable medium of claim 17 , wherein obtaining the set of network performance metrics includes:

retrieving one or more priority fault errors from a packet header of at least one of the plurality of data packets.

20 . The non-transitory computer-readable medium of claim 17 , wherein collecting the time series of data includes:

analyzing the source or the destination of the at least one of the plurality of data packets to determine a location reference associated with the source or the destination; and

correlating the location reference with location data.