CN107070817B - Uploading bandwidth optimization method applied to cloud live broadcast platform - Google Patents

Uploading bandwidth optimization method applied to cloud live broadcast platform Download PDF

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CN107070817B
CN107070817B CN201710351125.2A CN201710351125A CN107070817B CN 107070817 B CN107070817 B CN 107070817B CN 201710351125 A CN201710351125 A CN 201710351125A CN 107070817 B CN107070817 B CN 107070817B
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吴迪
叶国桥
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Sun Yat Sen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/70—Admission control; Resource allocation
    • H04L47/80—Actions related to the user profile or the type of traffic
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/70—Admission control; Resource allocation
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Abstract

The method and the device provided by the invention are based on the angle of a cloud live broadcast platform, reduce the bandwidth overhead of the uploader under the condition of limited uploading bandwidth, ensure the good watching experience of the user, consider the number of watching users of different uploaders, and provide the best possible user experience while reducing the traffic cost. The invention is based on an optimization framework of NBS (namely, Nash barring solution), can fairly and effectively distribute the uploading bandwidth to each participant in consideration of the number of different watching users of different uploaders under the condition of limited uploading bandwidth, and realizes global optimization while realizing optimal individual benefits.

Description

Uploading bandwidth optimization method applied to cloud live broadcast platform
Technical Field
The invention relates to the field of multimedia network and cloud computing resource management, in particular to an uploading bandwidth optimization method applied to a cloud live broadcast platform.
Background
With the wide coverage of video terminal devices and the rise of internet high-bandwidth consumption applications, cloud live platforms are emerging one after another and bring about a huge increase in network traffic. In recent years, many cloud live broadcast platforms have emerged at home and abroad, and many successful practices have been carried out. Domestic popular cloud live broadcast platforms comprise a goby live broadcast platform, a tiger-tooth live broadcast platform and a dragon ball live broadcast platform. The popular cloud live broadcast platforms abroad include Twitch.tv, YouTube and Azubu.tv. The content coverage of the cloud live platform is wide, and the cloud live platform relates to live game content, live entertainment and comprehensive art, live sports programs and the like.
The cloud live broadcast platform architecture mainly relates to three different groups: a video stream uploader, a cloud live platform and a viewer. The uploader can upload the video stream in real time by using various terminal devices (such as a personal computer, a smart phone, a tablet computer and the like), and the cloud live broadcast platform receives the video stream uploaded by the uploader, provides transcoding service and then distributes the transcoded video stream to the audience. Both the uploader and the audience may be distributed around the globe.
In the architecture of the cloud live broadcast platform, the transmission of the video stream mainly comprises three aspects: the uploading person uploads the video stream to the cloud live broadcast platform, the video in the cloud live broadcast platform is transmitted, and the cloud live broadcast platform distributes the video stream to audiences. The part of the invention that is concerned is mainly the upload part of the video stream.
With the increasing use of the internet, bandwidth becomes a limited resource, and how to fully and effectively utilize and allocate the bandwidth is an important issue. For a cloud live broadcast platform, the uploading bandwidth is limited, and as the number of uploaders continuously increases, the uploading bandwidth becomes a bottleneck of the cloud live broadcast platform. For a user, uploading video may incur traffic charges, and using different code rates (i.e., different video stream qualities) may incur different traffic charges. Then, in the existing cloud live broadcast platform architecture, an uploader can set an upload code rate at will, which will cause waste of upload bandwidth resources. In addition, in the service of the cloud live broadcast platform, videos uploaded by different uploaders have different audience numbers, and the quality of the videos uploaded by the uploaders greatly influences the user experience of the audiences. Therefore, it is a challenging problem to select the bitrate for uploading the video by the uploader under the condition of limited uploading bandwidth, and simultaneously consider the audience user experience of each uploader, and reasonably reduce the traffic cost brought by uploading the video.
Disclosure of Invention
The invention provides an uploading bandwidth optimization method applied to a cloud live broadcast platform, aiming at solving the problem that the uploading bandwidth distribution and the use of the existing cloud live broadcast platform are unreasonable.
In order to realize the purpose, the technical scheme is as follows:
an uploading bandwidth optimization method applied to a cloud live broadcast platform comprises the following steps:
s1, defining a set u ═ u1，u2，...，uNThe symbol represents the number of the uploaders,
Figure GDA0002560397350000021
representing a set formed by the uploading code rates selected by each uploader in the uploader group,
Figure GDA0002560397350000022
b represents the maximum uploading bandwidth of the cloud live broadcast platform; let r beminAnd
Figure GDA0002560397350000023
to represent the minimum upload bandwidth limit and the maximum upload bandwidth limit for each uploader, i.e.:
Figure GDA0002560397350000024
s2, defining bandwidth cost C of ith uploaderiComprises the following steps:
Ci＝ci*ri
wherein c isiThe traffic cost caused by unit bandwidth consumption is expressed;
s3, defining a QoE model of the viewing user of the ith uploader as follows:
Figure GDA0002560397350000025
defining a QoE model of a viewing user of the ith uploader when the ith uploader uploads the information with the minimum uploading bandwidth as follows:
Figure GDA0002560397350000026
s4, defining a utility model by combining the bandwidth overhead of the ith uploader and the QoE model of the viewing user to evaluate the current uploading code rate:
Figure GDA0002560397350000027
wherein k represents the weight of the bandwidth overhead;
defining the utility model of the ith uploader when the ith uploader uploads at the minimum uploading bandwidth as follows:
Figure GDA0002560397350000031
s5, combining the utility model obtained in the step S4 and the number Vi of viewing users of the ith uploader to define the utility function of the ith uploader as follows:
Figure GDA0002560397350000032
s6, the set u is set as u ═ u1，u2，...，uNExecuting the operations of the steps S1-S5 by each uploader to obtain a utility function of each uploader;
s7, defining the optimization problem of the uploading bandwidth as a Nash bargaining problem, and defining the Nash bargaining problem as follows:
P1：
Figure GDA0002560397350000033
wherein the set
Figure GDA0002560397350000034
Representing the uploading code rate selected by different uploaders, namely the target to be optimized;
s8, defining a corresponding P2 problem by combining the P1 problem:
P2：
Figure 100002_DEST_PATH_IMAGE002
s9, carrying out Lagrange transformation on the P2 problem to obtain a Lagrange function of the P2 problem as follows:
Figure GDA0002560397350000036
wherein
Figure GDA0002560397350000037
Gamma is all lagrange multipliers;
s10, decomposing the Lagrangian function, wherein the Lagrangian function is rewritten as:
Figure GDA0002560397350000038
wherein
Figure GDA0002560397350000041
S11. let each relate to liThe derivative of the Lagrange function is 0, and the optimal selection of the uploading code rate of the corresponding uploader is obtained, namely:
Figure GDA0002560397350000042
Figure GDA0002560397350000043
wherein
Figure GDA0002560397350000044
And the set is composed of uploading code rate optimal selection of each uploader obtained through Nash bargaining decision.
In a specific implementation process, after the optimal selection of the uploading code rate of each uploader is obtained by using an uploading bandwidth optimization method, the lagrangian multiplier needs to be selected
Figure GDA0002560397350000045
And gamma is subjected to update iteration, and the specific process is as follows:
the P2 problem is decomposed into multiple parts, and the P3 problem is converted into:
P3：Maxg(α，β，γ)
wherein
Figure GDA0002560397350000046
For a dual function, based on the Sub-gradient strategy, an update strategy of a Lagrange multiplier can be obtained:
(1) lagrange multiplier
Figure GDA0002560397350000047
The update policy of (1) is:
Figure GDA0002560397350000048
Figure GDA0002560397350000049
(2) lagrange multiplier
Figure GDA00025603973500000410
The update policy of (1) is:
Figure GDA0002560397350000051
Figure GDA0002560397350000052
(3) the update strategy for the lagrange multiplier γ is:
Figure GDA0002560397350000053
Figure GDA0002560397350000054
wherein s represents the order of iteration, ξ represents the step size of each iteration, and when | g (s +1) -g(s) | is less than or equal to upsilon, the lagrange multiplier is not used any more
Figure GDA0002560397350000055
Gamma is updated, where v is a set constant.
Compared with the prior art, the invention has the beneficial effects that:
the method and the device provided by the invention are based on the angle of a cloud live broadcast platform, reduce the bandwidth overhead of the uploader under the condition of limited uploading bandwidth, ensure the good watching experience of the user, consider the number of watching users of different uploaders, and provide the best possible user experience while reducing the traffic cost. The invention is based on an optimization framework of NBS (namely, Nash barring solution), can fairly and effectively distribute the uploading bandwidth to each participant in consideration of the number of different watching users of different uploaders under the condition of limited uploading bandwidth, and realizes global optimization while realizing optimal individual benefits.
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FIG. 1 is a schematic flow diagram of a method.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
the invention is further illustrated below with reference to the figures and examples.
Example 1
As shown in fig. 1, the method provided by the present invention specifically includes the following steps:
s1, defining a set u ═ u1，u2，...，uNThe symbol represents the number of the uploaders,
Figure GDA0002560397350000056
representing a set formed by the uploading code rates selected by each uploader in the uploader group,
Figure GDA0002560397350000057
b represents the maximum uploading bandwidth of the cloud live broadcast platform; let r beminAnd
Figure GDA0002560397350000058
to represent the minimum upload bandwidth limit and the maximum upload bandwidth limit for each uploader, i.e.:
Figure GDA0002560397350000061
s2, defining bandwidth cost C of ith uploaderiComprises the following steps:
Ci＝ci*ri
wherein c isiThe traffic cost caused by unit bandwidth consumption is expressed;
s3, defining a QoE model of the viewing user of the ith uploader as follows:
Figure GDA0002560397350000062
defining a QoE model of a viewing user of the ith uploader when the ith uploader uploads the information with the minimum uploading bandwidth as follows:
Figure GDA0002560397350000063
s4, defining a utility model by combining the bandwidth overhead of the ith uploader and the QoE model of the viewing user to evaluate the current uploading code rate:
Figure GDA0002560397350000064
wherein k represents the weight of the bandwidth overhead;
defining the utility model of the ith uploader when the ith uploader uploads at the minimum uploading bandwidth as follows:
Figure GDA0002560397350000065
s5, combining the utility model obtained in the step S4 and the number Vi of viewing users of the ith uploader to define the utility function of the ith uploader as follows:
Figure GDA0002560397350000066
s6, the set u is set as u ═ u1，u2，...，uNExecuting the operations of the steps S1-S5 by each uploader to obtain a utility function of each uploader;
s7, defining the optimization problem of the uploading bandwidth as a Nash bargaining problem, and defining the Nash bargaining problem as follows:
P1：
Figure GDA0002560397350000071
wherein the set
Figure GDA0002560397350000072
Representing the uploading code rate selected by different uploaders, namely the target to be optimized;
s8, defining a corresponding P2 problem by combining the P1 problem:
P2：
Figure 123821DEST_PATH_IMAGE002
s9, carrying out Lagrange transformation on the P2 problem to obtain a Lagrange function of the P2 problem as follows:
Figure GDA0002560397350000074
wherein
Figure GDA0002560397350000075
Gamma is all lagrange multipliers; r isi-rmin、
Figure GDA0002560397350000076
riB represents three constraints corresponding to the lagrange multiplier, including that the bandwidth cannot be greater than the maximum bandwidth, the bandwidth cannot be less than the minimum bandwidth, and the total bandwidth cannot be greater than the total upload bandwidth of the system.
S10, decomposing the Lagrangian function, wherein the Lagrangian function is rewritten as:
Figure GDA0002560397350000077
wherein
Figure GDA0002560397350000078
S11. let each relate to liThe derivative of the Lagrange function is 0 to obtain the corresponding uploading code of the uploaderOptimal selection of the rate, namely:
Figure GDA0002560397350000079
Figure GDA0002560397350000081
wherein
Figure GDA0002560397350000082
And the set is composed of uploading code rate optimal selection of each uploader obtained through Nash bargaining decision.
In the scheme, the method provided by the invention is mainly used for optimizing the uploading bandwidth, and does not consider the transcoding of the cloud live broadcast platform and the part for transmitting the video stream to the audience. Therefore, the QoE of the user is determined by the video bitrate uploaded by the uploader, and the method can be understood that after the uploader uploads the video at a bitrate, the watching user of the uploader has an opportunity to watch the video at the bitrate.
In the above scheme, the present invention adopts an optimization strategy of NBS (i.e., Nash locking solution, Nash Bargaining solution). The underlying idea of NBS is that a single participant can calculate his optimal selection assuming the selection strategy of the other participants is unchanged, and no other participant can use the other selection to gain more utility when the other participants do not change their selection strategy. The resource allocation strategy based on the game theory can ensure fairness and effectiveness, and can ensure the maximization of global utilization while ensuring the optimal benefit of individuals.
In a specific implementation process, after the optimal selection of the uploading code rate of each uploader is obtained by using an uploading bandwidth optimization method, the lagrangian multiplier needs to be selected
Figure GDA0002560397350000083
And gamma is subjected to update iteration, and the specific process is as follows:
the P2 problem is decomposed into multiple parts, and the P3 problem is converted into:
P3：Maxg(α，β，γ)
wherein
Figure GDA0002560397350000084
For a dual function, based on the Sub-gradient strategy, an update strategy of a Lagrange multiplier can be obtained:
(1) lagrange multiplier
Figure GDA0002560397350000085
The update policy of (1) is:
Figure GDA0002560397350000091
Figure GDA0002560397350000092
(2) lagrange multiplier
Figure GDA0002560397350000093
The update policy of (1) is:
Figure GDA0002560397350000094
Figure GDA0002560397350000095
(3) the update strategy for the lagrange multiplier γ is:
Figure GDA0002560397350000096
Figure GDA0002560397350000097
wherein s represents the order of iteration, ξ represents the step size of each iteration, and when | g (s +1) -g(s) | is less than or equal to upsilon, the lagrange multiplier is not used any more
Figure GDA0002560397350000098
Gamma is updated, where v is a set constant.
The pseudo code of the optimization method provided by the invention is as follows:
Figure GDA0002560397350000099
Figure GDA0002560397350000101
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (2)

1. An uploading bandwidth optimization method applied to a cloud live broadcast platform is characterized by comprising the following steps: the method comprises the following steps:
s1. definition set
Figure FDA0002560397340000011
A group of the uploaders is represented,
Figure FDA0002560397340000012
representing a set formed by the uploading code rates selected by each uploader in the uploader group,
Figure FDA0002560397340000013
b represents the maximum uploading bandwidth of the cloud live broadcast platform; let r beminAnd
Figure FDA0002560397340000014
to represent the minimum upload bandwidth limit and the maximum upload bandwidth limit for each uploader, i.e.:
Figure FDA0002560397340000015
s2, defining bandwidth cost C of ith uploaderiComprises the following steps:
Ci＝ci*ri
wherein c isiThe traffic cost caused by unit bandwidth consumption is expressed;
s3, defining a QoE model of the viewing user of the ith uploader as follows:
Figure FDA0002560397340000016
defining a QoE model of a viewing user of the ith uploader when the ith uploader uploads the information with the minimum uploading bandwidth as follows:
Figure FDA0002560397340000017
s4, defining a utility model by combining the bandwidth overhead of the ith uploader and the QoE model of the viewing user to evaluate the current uploading code rate:
Figure FDA0002560397340000018
wherein k represents the weight of the bandwidth overhead;
defining the utility model of the ith uploader when the ith uploader uploads at the minimum uploading bandwidth as follows:
Figure FDA0002560397340000019
s5, combining the utility model obtained in S4 and the number V of viewing users of the ith uploaderiDefining the utility function of the ith uploader as:
Figure FDA0002560397340000021
s6, pair set
Figure FDA0002560397340000029
Each uploader performs the operations of the steps S1-S5 to obtain a utility function of each uploader;
s7, defining the optimization problem of the uploading bandwidth as a Nash bargaining problem, and defining the Nash bargaining problem as follows:
P1：
Figure FDA0002560397340000022
wherein the set
Figure 2
Representing the uploading code rate selected by different uploaders, namely the target to be optimized;
s8, defining a corresponding P2 problem by combining the P1 problem:
P2：
Figure DEST_PATH_IMAGE002
s9, carrying out Lagrange transformation on the P2 problem to obtain a Lagrange function of the P2 problem as follows:
Figure FDA0002560397340000025
wherein
Figure FDA0002560397340000026
Gamma is all lagrange multipliers;
s10, decomposing the Lagrangian function, wherein the Lagrangian function is rewritten as:
Figure FDA0002560397340000027
wherein
Figure FDA0002560397340000028
S11. let each relate to liThe derivative of the Lagrange function is 0, and the optimal selection of the uploading code rate of the corresponding uploader is obtained, namely:
Figure FDA0002560397340000031
Figure FDA0002560397340000032
wherein
Figure FDA0002560397340000033
And the set is composed of uploading code rate optimal selection of each uploader obtained through Nash bargaining decision.
2. The upload bandwidth optimization method applied to the cloud live platform according to claim 1, wherein:
after the optimal selection of the uploading code rate of each uploader is obtained by using an uploading bandwidth optimization method, the Lagrange multiplier needs to be adjusted
Figure FDA0002560397340000034
And gamma is subjected to update iteration, and the specific process is as follows:
the P2 problem is decomposed into multiple parts, and the P3 problem is converted into:
P3:Max g(α，β，γ)
wherein
Figure FDA0002560397340000035
For a dual function, based on the Sub-gradient strategy, an update strategy of a Lagrange multiplier can be obtained:
(1) lagrange multiplier
Figure FDA0002560397340000036
The update policy of (1) is:
Figure FDA0002560397340000037
Figure FDA0002560397340000038
(2) lagrange multiplier
Figure FDA0002560397340000039
The update policy of (1) is:
Figure FDA00025603973400000310
Figure FDA00025603973400000311
(3) the update strategy for the lagrange multiplier γ is:
Figure FDA0002560397340000041
Figure FDA0002560397340000042
wherein s represents the order of iteration, ξ represents the step size of each iteration, and when | g (s +1) -g(s) | ≦ v, the lagrange multiplier is no longer applied
Figure FDA0002560397340000043
Gamma is updated, where v is a set constant.
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