Machine learning for adaptive bitrate selection

Patent metadata

Field	Value
Publication number	US-12137265-B2
Application number	US-202318462635-A
Country	US
Kind code	B2
Filing date	Sep 7, 2023
Priority date	Oct 29, 2021
Publication date	Nov 5, 2024
Grant date	Nov 5, 2024

Abstract

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

First claim

What is claimed is:

1. A computer-implemented method for adaptive bitrate selection, comprising:
receiving, by at least one computer processor, a data streaming request;
predicting, by a speed predictive machine learning model and based on one or more streaming parameters, a current sustainable network bandwidth, wherein the speed predictive machine learning model is trained using a training data set comprising a history of the one or more streaming parameters;
predicting, by a rebuffer predictive machine learning model and based on the current sustainable network bandwidth, a buffer level of a data buffer, and a chunk duration, a candidate bitrate at which a likelihood of rebuffering of the data buffer occurs less than a threshold percentage;
selecting, based on the candidate bitrate, a download bitrate to complete the data streaming request; and
downloading streaming data at the download bitrate.

2. The computer-implemented method of claim 1 , wherein the predicting the candidate bitrate further comprises:
predicting, by the rebuffer predictive machine learning model and based further on N available discrete bitrates, the candidate bitrate at which the likelihood of rebuffering of the data buffer occurs less than the threshold percentage.

3. The computer-implemented method of claim 2 , wherein the predicting the candidate bitrate further comprises:
selecting the current sustainable network bandwidth as a first instance of the candidate bitrate.

4. The computer-implemented method of claim 3 , wherein the predicting the candidate bitrate further comprises:
predicting that the first instance of the candidate bitrate would produce the rebuffering of the data buffer; and
selecting, from the N available discrete bitrates, a second instance of the candidate bitrate.

5. The computer-implemented method of claim 4 , wherein the selecting, from the N available discrete bitrates, the second instance of the candidate bitrate further comprises:
selecting a next lower bitrate from the N available discrete bitrates.

6. The computer-implemented method of claim 1 , further comprising:
retraining the speed predictive machine learning model based on the current sustainable network bandwidth; and
retraining the rebuffer predictive machine learning model based on the candidate bitrate.

7. The computer-implemented method of claim 1 , further comprising:
in response to the likelihood of rebuffering of the data buffer being over a threshold percentage, selecting a next lower available bitrate until the likelihood of rebuffering of the data buffer is under the threshold percentage.

8. The computer-implemented method of claim 1 , wherein the one or more streaming parameters further comprise any of:
N previous bitrates, where N represents a number of most recent bitrates in a history of previous download bitrates;
an average speed of the N previous bitrates; or
a standard deviation of the N previous bitrates.

9. A system for adaptive bitrate selection, comprising:
one or more memories;
at least one processor each coupled to at least one of the memories and configured to perform operations comprising:
receiving a data streaming request;
predicting, by a speed predictive machine learning model and based on one or more of a plurality of streaming parameters, a current sustainable network bandwidth, wherein the speed predictive machine learning model is trained using a training data set comprising a history of the one or more streaming parameters;
predicting, by a rebuffer predictive machine learning model and based on the current sustainable network bandwidth, a buffer level of a data buffer, and a chunk duration, a candidate bitrate at which a likelihood of rebuffering of the data buffer occurs less than a threshold percentage;
selecting, based on the candidate bitrate, a download bitrate to complete the streaming request; and
downloading streaming data at the download bitrate.

10. The system of claim 9 , wherein the predicting the candidate bitrate further comprises:
predicting, by the rebuffer predictive machine learning model and based further on N available discrete bitrates, the candidate bitrate at which the likelihood of rebuffering of the data buffer occurs less than the threshold percentage.

11. The system of claim 10 , wherein the predicting the candidate bitrate further comprises:
selecting the current sustainable network bandwidth as a first instance of the candidate bitrate.

12. The system of claim 11 , wherein the predicting the candidate bitrate further comprises:
predicting that the first instance of the candidate bitrate would produce the rebuffering of the data buffer; and
selecting, from the N available discrete bitrates, a second instance of the candidate bitrate.

13. The system of claim 12 , wherein the selecting, from the N available discrete bitrates, the second instance of the candidate bitrate further comprises:
selecting a next lower bitrate from the N available discrete bitrates.

14. The system of claim 9 , wherein the operations further comprise:
retraining the speed predictive machine learning model based on the current sustainable network bandwidth; and
retraining the rebuffer predictive machine learning model based on the candidate bitrate.

15. The system of claim 9 , wherein the operations further comprise:
in response to the likelihood of rebuffering of the data buffer being over a threshold percentage, selecting a next lower available bitrate until the likelihood of rebuffering of the data buffer is under the threshold percentage.

16. The system of claim 9 , wherein the one or more streaming parameters further comprise any of:
N previous bitrates, where N represents a number of most recent bitrates in a history of previous download bitrates;
an average speed of the N previous bitrates; or
a standard deviation of the N previous bitrates.

17. A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
receiving a data streaming request;
predicting, by a speed predictive machine learning model and based on one or more of a plurality of streaming parameters, a current sustainable network bandwidth, wherein the speed predictive machine learning model is trained using a training data set comprising a history of the one or more streaming parameters;
predicting, by a rebuffer predictive machine learning model and based on the one or more streaming parameters, the current sustainable network bandwidth, a buffer level of a data buffer, and a chunk duration, a candidate bitrate at which a likelihood of rebuffering of the data buffer occurs less than a threshold percentage;
selecting, based on the candidate bitrate, a download bitrate to complete the streaming request; and
downloading streaming data at the download bitrate.

18. The non-transitory computer-readable medium of claim 17 , wherein the predicting the candidate bitrate further comprises:
predicting, by the rebuffer predictive machine learning model and based further on N available discrete bitrates, the candidate bitrate at which the likelihood of rebuffering of the data buffer occurs less than the threshold percentage.

19. The non-transitory computer-readable medium of claim 18 , wherein the predicting the candidate bitrate further comprises:
selecting the current sustainable network bandwidth as a first instance of the candidate bitrate.

20. The no

Assignees

• Roku Inc

Inventors

• Paliwal Amit
• Marsavin Andrey
• Vaidya Govind
• Michiels Wim
• Logan Beth Teresa
• Han Zheng
• Oza Tapan
• Raghavan Vijay Anand

Classifications

• H04N21/44004 Primary

  involving video buffer management, e.g. video decoder buffer or video display buffer · CPC title

• H04N21/8456

  by decomposing the content in the time domain, e.g. in time segments · CPC title

• H04N21/44209

  Monitoring of downstream path of the transmission network originating from a server, e.g. bandwidth variations of a wireless network (arrangements for maintenance or administration in data switching networks involving bandwidth and capacity management H04L41/0896) · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title