IP Library › Granted Patent US 12,192,591
Granted Patent B2
US 12,192,591 · App. 17/878,813 · Granted Jan 7, 2025

Training an encrypted video stream network scoring system with non-reference video scores

Inventors: Michael Colligan (Sunnyvale, CA); Jeremy Bennington (Greenwood, IN)
Assignee: Spirent Communications, Inc.
H04N21/64738H04N21/23418H04N21/64784
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,192,591
App. No.
17/878,813
Granted
Jan 7, 2025
Kind
B2
Abstract

At least three uses of the technology disclosed are immediately recognized. First, a video stream classifier can be trained that has multiple uses. Second, a trained video stream classifier can be applied to monitor a live network. It can be extended by the network provider to customer relations management or to controlling video bandwidth. Third, a trained video stream classifier can be used to infer bit rate switching of codecs used by video sources and content providers. Bit rate switching and resulting video quality scores can be used to balance network loads and to balance quality of experience for users, across video sources. Balancing based on bit rate switching and resulting video quality scores also can be used when resolving network contention.

Claims (29)

1. A non-transitory computer readable media impressed with program instructions that, when executed on hardware, cause the hardware to perform a method of monitoring video quality of delivered video streams on a live network, the method including:

measuring network conditions including actual bit rate during delivery of numerous video streams at a plurality of locations on the live network, correlated with data identifying a video source per video stream;

applying a trained classifier to the measured network conditions and the correlated data to assign video quality scores without dependence on rendering images from the video streams;

aggregating the assigned video quality scores based on one or more parameters of the measured network conditions and the identifying data; and

storing at least the aggregated video quality scores.

2. The non-transitory computer readable media of claim 1 , further including instructions, that when executed, cause the hardware to perform the method, wherein the plurality of locations include 100 to 1,000,000 physical locations on the live network.

3. The non-transitory computer readable media of claim 1 , further including instructions, that when executed, cause the hardware to perform the method, further including:

the numerous video streams at the plurality of locations on the live network further correlated with data identifying a recipient device type per the video stream.

4. The non-transitory computer readable media of claim 1 , further including instructions, that when executed, cause the hardware to perform the method, further including:

the numerous video streams at the plurality of locations on the live network further correlated with data identifying a recipient user per the video stream.

5. The non-transitory computer readable media of claim 1 , further including instructions, that when executed, cause the hardware to perform the method, further including:

raising an alert to a network operating center when the aggregated video quality scores for a portion of the live network reach an alert level.

6. A method of monitoring video quality of delivered video streams on a live network, the method including executing program instructions from the non-transitory computer readable media of claim 1 on the hardware.

7. A device configurable to monitor video quality of delivered video streams on a live network, the device including the program instructions from the non-transitory computer readable media of claim 1 and the hardware adapted to execute the program instructions.

8. A non-transitory computer readable media impressed with program instructions that, when executed on hardware, cause the hardware to perform a method of mapping video quality against available bandwidth for a video source over a live network, the method including:

repeatedly requesting that the video source deliver selected videos while systematically impairing network conditions at a node of the live network, including setting the available bandwidth;

measuring actual delivered bit rate from the video source under the impaired network conditions;

inferring a bit rate table of the video source from variation in the actual delivered bit rate during the systematically impairing the network conditions;

applying a trained video stream classifier to determine the video quality delivered by the video source over the live network based on characteristics of the impaired network conditions and/or on actual delivered bit rate; and

saving the inferred bit rate table and the determined video quality for the video source correlated with the impaired network conditions.

9. The non-transitory computer readable media of claim 8 , further including instructions, that when executed, cause the hardware to perform the method, wherein the trained video stream classifier determines the video quality scores without dependence on rendering images from the video streams.

10. The non-transitory computer readable media of claim 8 , further including instructions, that when executed, cause the hardware to perform the method, further including:

selecting the video examples to include variety of scene types that vary in image complexity, lighting and color.

11. The non-transitory computer readable media of claim 8 , further including instructions, that when executed, cause the hardware to perform the method, further including:

applying the requesting, measuring, inferring, applying and saving to a plurality of receiving devices of different brands and models and using the receiving device brand and model as elements of the ground truth for the training.

12. The non-transitory computer readable media of claim 8 , further including instructions, that when executed, cause the hardware to perform the method, further including:

using at least the inferred bit rate table and the determined video quality for the video source to set available bit rate for particular recipients of live video streams on a live network.

13. A method of mapping video quality against available bandwidth for a video source over a live network, the method including executing program instructions from the non-transitory computer readable media of claim 8 on the hardware.

14. A device configurable to map video quality against available bandwidth for a video source over a live network, the device including the program instructions from the non-transitory computer readable media of claim 8 and the hardware adapted to execute the program instructions.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 1, 2022
From: COLLIGAN, MICHAEL; BENNINGTON, JEREMY
To: SPIRENT COMMUNICATIONS, INC.
Reel/Frame 060690/0288 →
Continuity (3)
Division 16842676 · Apr 7, 2020
Provisional Application 62831114 · Apr 8, 2019
Related Publication 20220368995A1 · Nov 17, 2022
References Cited (49)
US 4991007A · Corley · 1991 [cited by applicant]
US 5181098A · Guerin et al. · 1993 [cited by applicant]
US 5351201A · Harshbarger, Jr. et al. · 1994 [cited by applicant]
US 6542185B1 · Bogardus · 2003 [cited by applicant]
US 9380297B1 · Djurdjevic · 2016 [cited by applicant]
US 9591300B2 · Djurdjevic · 2017 [cited by applicant]
US 20040066454A1 · Otani et al. · 2004 [cited by applicant]
US 20040085256A1 · Hereld et al. · 2004 [cited by applicant]
US 20050020357A1 · Oe · 2005 [cited by applicant]
US 20070091201A1 · Sasaki · 2007 [cited by applicant]
US 20070091334A1 · Yamaguchi et al. · 2007 [cited by applicant]
US 20070217448A1 · Luo · 2007 [cited by examiner]
US 20100157047A1 · Larkin et al. · 2010 [cited by applicant]
US 20100309231A1 · Stauder et al. · 2010 [cited by applicant]
US 20110102602A1 · Leventu et al. · 2011 [cited by applicant]
US 20130027568A1 · Zou et al. · 2013 [cited by applicant]
US 20130057705A1 · Parker · 2013 [cited by examiner]
US 20130293725A1 · Zhang et al. · 2013 [cited by applicant]
US 20130305106A1 · Mittal et al. · 2013 [cited by applicant]
US 20140176730A1 · Kaji et al. · 2014 [cited by applicant]
US 20150135209A1 · LaBosco et al. · 2015 [cited by applicant]
US 20150279037A1 · Griffin et al. · 2015 [cited by applicant]
US 20150365725A1 · Belyaev et al. · 2015 [cited by applicant]
US 20160165225A1 · Djurdjevic · 2016 [cited by applicant]
US 20160165226A1 · Djurdjevic · 2016 [cited by applicant]
US 20160353138A1 · Bennington · 2016 [cited by applicant]
US 20160358321A1 · Xu et al. · 2016 [cited by applicant]
US 20190138938A1 · Vasseur · 2019 [cited by examiner]
US 20190334824A1 · Jana · 2019 [cited by examiner]
CN 108090902A · 2018 [cited by applicant]
Series G: Transmission Systems and Media, Digital Systems and Networks Multimedia Quality of Service and performance—Generic and user-related aspects (ITU-T, G.1022), International Telecommunication Union, Jul. 2016, 34… [cited by applicant]
Series P: Telephone Transmission Quality, Telephone Installations, Local Line Networks Models and tools for quality assessment of streamed media (ITU-T, P.1203). International Telecommunication Union, Oct. 2017, 22 page… [cited by applicant]
Per-Title Encode Optimization, Netflix Technology Blog, Dec. 14, 2015, 21 pages (downloaded from https://medium.com/netflix-techblog/per-title-encode-optimization-7e99442b62a2, Jan. 29, 2019. [cited by applicant]
Kayargadde et al., “Perceptual characterization of images degraded by blur and noise: model”, Institute for Perception Research, P.O. Box 513, 5600 MB Eindhoven, The Netherlands, Journal of the Optical Society of Americ… [cited by applicant]
Seshadrinathan, et al., “A Subjective Study to Evaluate Video Quality Assessment Algorithms”, Intel Corporation, Chandler, AZ—USA. The University of Texas at Austin, TX—USA., Jan. 1, 2010, 10 pages. [cited by applicant]
Farias, et al., “Perceptual Contributions of Blocky, Blurry, and Fuzzy Impairments to Overall Annoyance”, Proceedings of SPIE, vol. 5292, Department of Electrical and Computer Engineering, Department of Psychology, UCSB… [cited by applicant]
Xu, “Video Telephony for End-consumers: Measurement Stufy of Google+, iChat, and Skype,” Networking, IEEE/ACM Transactions, vol. 22, Iss.3, 2014, pp. 17. [cited by applicant]
Castaner, “Why We Didn't Implement Mean Opinion Score (MOS) in Avalananche Adaptive Bitrate,” Spirent Blogs, Nov. 30, 2012, pp. 5. [cited by applicant]
“8100 Mobile Device Test System UMTS Video User Expereince Module,” Spirent Communications Inspired Innovation, Rev. A 07/08, 2008, pp. 2. [cited by applicant]
Videoconferencing (VC), accessed Mar. 19, 2015, <http://en.wikipedia.org/wiki/Videoconferencing> pp. 4. [cited by applicant]
H. Schulzrinne, et al., “RTP: A Transport Protocol for Real-Time Applications,” RFC 11889, (2015), pp. 150. [cited by applicant]
“Opinion model predicting gaming quality of experience for cloud gaming services”, Series G: Transmission Systems and Media, Digital Systems and Networks Multimedia Quality of Service and performance—Generic and user-re… [cited by applicant]
“Opinion model for network planning of video and audio streaming applications”, Series G: Transmission Systems and Media, Digital Systems and Networks Multimedia Quality of Service and performance—Generic and user-relat… [cited by applicant]
Andrea Di Domenico et al, “A Network Analysis on Cloud Gaming—Stadia: GeForce Now and PSNow”, MDPI Network (2021). DOI: https://doi.org/10.3390/network1030015. [cited by applicant]
Castaner, “Why We Didn't Implement Mean Opinion Score (MOS) in Avalanche Adaptive Bitrate,” Spirent Blogs, Nov. 30, 2012, pp. 5. [cited by applicant]
U.S. Appl. No. 18/227,885, filed Jul. 28, 2023, Pending. [cited by applicant]
U.S. Appl. No. 16/842,676, filed Dec. 11, 2018, U.S. Pat. No. 11,216,698, Jan. 4, 2022, Granted. [cited by applicant]
U.S. Appl. No. 14/667,557, filed Mar. 24, 2015, U.S. Pat. No. 9,591,300, Mar. 7, 2017, Granted. [cited by applicant]
U.S. Appl. No. 14/667,540, filed Mar. 24, 2015, U.S. Pat. No. 9,380,297, Jun. 28, 2016, Granted. [cited by applicant]