Bandwidth and ABR video QoE management based on OTT video providers and devices

Patent metadata

Field	Value
Publication number	US-10939153-B2
Application number	US-201815888903-A
Country	US
Kind code	B2
Filing date	Feb 5, 2018
Priority date	Jul 7, 2016
Publication date	Mar 2, 2021
Grant date	Mar 2, 2021

Abstract

An Adaptive Bitrate (ABR) Quality of Experience (QoE) Management Unit manages bandwidth usage and QoE at a customer premises where multiple client devices independently download content from multiple providers. An overall virtual pipe to the premises includes a Hypertext Transfer Protocol (HTTP) inner pipe, a non-HTTP inner pipe, an ABR inner pipe, and a non-ABR inner pipe. The Unit determines a data cap for a current billing cycle; and determines, based on policy management settings and the data cap for the current billing cycle, at least one of: a bandwidth cap for the overall virtual pipe, a bandwidth cap for the HTTP inner pipe, a bandwidth cap for the non-HTTP inner pipe, a bandwidth cap for the ABR inner pipe, and a bandwidth cap for the non-ABR inner pipe. Responsive to the determination of the bandwidth caps, the Unit throttles traffic within at least one of the inner pipes.

First claim

What is claimed is:

1. A method in a computer-controlled Adaptive Bitrate (ABR) Quality of Experience (QoE) Management Unit for managing bandwidth usage and QoE at a customer premises where multiple client devices independently download content from multiple providers, the method comprising:
determining a data cap for a current billing cycle day, the data cap associated with a network provider;
determining, based on policy management settings and the data cap for the current billing cycle day, a premises bandwidth cap for an overall pipe to the premises; and
responsive to the determination of the bandwidth cap, throttling traffic within the overall pipe to the premises, wherein the traffic comprises content downloaded from multiple different over the top (OTT) providers.

2. The method according to claim 1 , further comprising:
predicting future data usage at the customer premises for the current billing cycle;
monitoring all actual data usage at the premises; and
dynamically adjusting the bandwidth cap throughout the current billing cycle, using the predicted future data usage as an input, to smoothly keep actual total data usage for the current billing cycle from exceeding a billing cycle data cap for the current billing cycle before the current billing cycle ends.

3. The method according to claim 2 , wherein predicting future data usage includes:
generating a linear regression model for future data usage at the customer premises based on past data usage at the premises, wherein the linear regression model utilizes a least square method; and
dynamically updating the linear regression model by recalculating coefficients as more recent data usage information becomes available.

4. The method according to claim 2 , wherein predicting future data usage includes generating a historical model for data usage at the customer premises based on historical data usage patterns at the premises during past billing cycles.

5. The method according to claim 4 , further comprising:
throttling only ABR data streams to maintain a current premises bitrate within the premises bandwidth cap, wherein throttling includes:
generating a value, α, equal to the time in the billing cycle (t m ) divided by the time spent consuming video (t c ) based on the historical data usage patterns at the premises during past billing cycles;
calculating, for the sum of all ABR data streams entering the premises, an allowed data rate (ABR_Rate) by multiplying a by the data remaining within the data cap divided by the days remaining in the current billing cycle; and
setting a bitrate limit for each ABR data stream based on the ABR_Rate and the number of ABR data streams.

6. The method according to claim 1 , further comprising maintaining a desired QoE for each client device/provider combination by:
assigning a priority level to each provider and to each client device;
translating the assigned priority levels into weights; and
utilizing the weights in a Weighted Fair Queuing (WFQ) algorithm to control, at any given time, an amount of bandwidth each client device is allowed to utilize to download content from any given provider, thereby maintaining a desired QoE for each client device/provider combination.

7. The method according to claim 6 , wherein utilizing the weights in a WFQ algorithm includes utilizing Phantom Packet Transmission (PPT) WFQ to prevent a first client device from increasing its bit rate when a second client device is in an idle phase of its duty cycle due to a full buffer.

8. The method according to claim 7 , wherein utilizing PPT WFQ includes:
detecting that the second client device has entered the idle phase of its duty cycle; and
generating phantom packets that are not associated with any actual payload traffic, wherein the phantom packets simulate a continuous network demand by the second client device while the second client device is in the idle phase of its duty cycle, thereby preventing the first client device from increasing its bit rate.

9. The method according to claim 8 , wherein utilizing PPT WFQ also includes:
selecting packets for transmission to the client devices;
when a real packet containing actual payload traffic for an associated client device is selected for transmission, transmitting the selected real packet to the associated client device; and
when a phantom packet is selected for transmission, transmitting a replacement real packet to a client device that is utilizing a data stream that does not exhibit a duty cycle.

10. A Quality of Experience (QoE) Management system for managing bandwidth usage and QoE at a customer premises where multiple client devices independently download content from multiple providers, the QoE Management system comprising:
at least one microprocessor;
a non-transitory computer-readable medium coupled to the at least one microprocessor configured to store computer-readable instructions, wherein when the instructions are executed by the at least one microprocessor, the QoE Management system is caused to:
determine a data cap for a current billing cycle day, the data cap associated with a network provider;
determine, based on policy management settings and the data cap for the current billing cycle day, a premises bandwidth cap for an overall pipe to the premises; and
responsive to the determination of the bandwidth caps, throttle traffic within the overall pipe to the premises, wherein the traffic comprises content downloaded from multiple different over the top (OTT) providers.

11. The QoE Management system according to claim 10 , wherein the Management system is configured to:
predict future data usage at the customer premises for the current billing cycle;
monitor all actual data usage at the premises; and
dynamically adjust the bandwidth caps throughout the current billing cycle, using the predicted future data usage as an input, to smoothly keep actual total data usage for the current billing cycle from exceeding a billing cycle data cap for the current billing cycle before the current billing cycle ends.

12. The QoE Management system according to claim 11 , wherein the Management system is configured to predict future data usage by:
generating a linear regression model for future data usage at the customer premises based on past data usage at the premises, wherein the linear regression model utilizes a least square method; and
dynamically updating the linear regression model by recalculating coefficients as more recent data usage information becomes available.

13. The QoE Management system according to claim 11 , wherein predicting future data usage includes generating a historical model for data usage at the customer premises based on historical data usage patterns at the premises during past billing cycles.

14. The QoE Management system according to claim 13 , further comprising:
throttling only Adaptive Bit Rate (ABR) data streams to maintain a current premises bitrate within the premises bandwidth cap, wherein throttling includes:
generating a value, α, equal to the time in the billing cycle (t m ) divided by the time spent consuming video (t c ) based on the historical data usage patterns at the premises during past billing cycles;
calculating, for the sum of all ABR data streams entering the premises, an allowed data rate (ABR_Rate) by multiplying a by the data remaining within the data cap divided by the days remaining in the current billing cycle; and
setting a bitrate limit for each ABR data stream based on the ABR_Rate and the number of ABR data streams.

15. The QoE Management system according to claim 10 , further comprising mai

Assignees

• Ericsson Telefon Ab L M

Inventors

• Phillips Chris
• Forsman Robert Hammond
• Reynolds Jennifer Ann
• Changrani Vishal

Classifications

• H04N21/2662 Primary

  Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities · CPC title

• H04L12/14 Primary

  Charging, metering or billing arrangements specially adapted for data communications, e.g. authentication, authorisation and accounting [AAA] framework · CPC title

• H04M15/8214

  Data or packet based · CPC title

• H04M15/8228

  Session based · CPC title

• H04M15/66

  Policy and charging system · CPC title