CN103200592A - Optimal resource distribution method in LTE streaming medium communication based on quality of experience (QoE) - Google Patents

Optimal resource distribution method in LTE streaming medium communication based on quality of experience (QoE) Download PDF

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CN103200592A
CN103200592A CN2013100584959A CN201310058495A CN103200592A CN 103200592 A CN103200592 A CN 103200592A CN 2013100584959 A CN2013100584959 A CN 2013100584959A CN 201310058495 A CN201310058495 A CN 201310058495A CN 103200592 A CN103200592 A CN 103200592A
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user
enodeb
length
testing speech
increment
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CN103200592B (en
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周亮
吴丹
陈建新
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Nanjing Tian Gu Information Technology Co ltd
Information and Telecommunication Branch of State Grid Jiangsu Electric Power Co Ltd
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Nanjing Post and Telecommunication University
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Abstract

Provided is an optimal resource distribution method in LTE streaming medium communication based on quality of experience (QoE). Dynamic resource distribution based on an on-line detection-optimizing strategy is designed, so that resource optimization distribution by eNodeB on the condition that QoE model information of each user is not complete is achieved. Particularly, uncertainty of a QoE model and streaming medium playing time is considered in a combined mode, and accordingly optimal compromise is obtained on detection accuracy and optimization performance.

Description

Based on the optimal resource allocation method in the LTE streaming media communication of QoE
Technical field
What the present invention relates to is a kind of for the optimal resource allocation method based on the LTE streaming media communication of QoE, specifically be a kind ofly to consider QoE model and the uncertain situation of streaming media playing time uniting, by the Dynamic Resource Allocation for Multimedia of design based on on-line testing-optimisation strategy, to be implemented in measuring accuracy and to optimize the resource optimal distribution method of obtaining optimal compromise on the performance.
Background technology
Along with popularizing of broadband network and deepening continuously of LTE research, stream media technology has obtained development at full speed, day by day become one of the most popular business in the LTE network, and the application system of Streaming Media, international standard and basic research also become the focus that present industry and scientific research are paid close attention to.But notably be to realize that Streaming Media is applied in popularizing of every field, needs to improve the service quality that Streaming Media is used.In order to reach this target, to Internet resources reasonably distribute and manage essential; Otherwise when using constantly increase, network performance will significantly descend along with the minimizing of Internet resources.This shows, the optimization of Internet resources distributes the key of being not only network stabilization, efficient operation, simultaneously also be basis and the prerequisite that realizes various service quality, particularly demand rate, the duration of network are all used much bigger Streaming Media than tradition and use, seem more most important.In addition, consider that actual LTE network is the system of continuous development and change, the research that the optimization of its resource is distributed will be one and have challenging research field.
It mainly is to describe corresponding service quality with network QoS (Quality of Service) parameter that tradition solves thinking, to realize the resource high-efficiency reasonable configuration.Particularly, QoS mainly is responsible for carrying out service management and professional otherness being provided from the angle of network, and network entity is handled different business according to different quality requirements.Unfortunately, because the Streaming Media transmission is comparatively responsive to time delay, require comparatively harsh to packet loss and the error rate, add the variation of network access mode and the isomerism of the network terminal, make original network QoS framework need take into full account end-to-end user's experience, particularly at different classes of performance of services index, more feasible mode is that the angle from the user is defined and describes.So, QoE(Quality of Experience) framework arises at the historic moment.It is defined as the appreciable service quality of user, i.e. the subjective feeling of terminal use's communication service performance that network is provided.Because it is more accurate aspect the description user's request, so can accurately define the stream media service system optimization aim.
In fact, the resource optimization based on QoE in the streaming media communication distributes research to obtain extensive concern, but great majority research supposes that all each user's QoE model is known for system.Yet most of actual conditions are that the information of relevant QoE model is incomplete, and for example, the user side by side selects polytype streaming media service arbitrarily; Multimedia application in the DYNAMIC COMPLEX environment etc.In these situations, be difficult to even may not obtain complete QoE model information in advance sometimes.In addition because the particularity of streaming media communication, its reproduction time usually not as can be known yet, this has further deepened the difficulty that resource allocation problem solves.
To this class problem, the existing thinking that solves mainly is divided into two classes, i.e. theoretical the and stochastic approximation theory of Bayesian Estimation.The former inferior position is to depend on to a great extent the prior distribution to unknown parameter, and this is still unfeasible for the actual flow media communication.And the latter need borrow power in a large amount of historical informations, and its computation complexity is higher, is unsuitable for the real-time online operation.In addition, consider the particularity of streaming media communication, for example its reproduction time also is unknown, can't guarantee more that therefore a large amount of real data can be observed.
Given this, resource under incomplete at QoE model information in the streaming media communication is distributed, to there be many problem demanding prompt solutions: in streaming media playing in the time, be spending some times, to test the QoE model feasible? does how designing suitable on-line testing-optimisation strategy solve and reduces the testing time and strengthen intrinsic contradiction between the estimated accuracy? in actual multimedia communications system, how does this implement this strategy again? in one word, the existence of these problems has seriously restricted the raising of the service quality of Streaming Media application, needs further be researched and solved.
Summary of the invention
The objective of the invention is to overcome the deficiencies in the prior art, a kind of being applicable to based on the optimal resource allocation method in the LTE streaming media communication of QoE proposed, the present invention can unite consideration QoE model and the uncertainty of streaming media playing time, thereby obtains optimal compromise at measuring accuracy and optimization performance., distribute to be implemented in the resource optimization of eNodeB when incomplete to each user QoE model information based on the Dynamic Resource Allocation for Multimedia of on-line testing-optimisation strategy by design.
The present invention is achieved by the following technical solutions:
The first step: in a LTE network, there is an eNodeB who is positioned at the optional position,
Figure 184954DEST_PATH_IMAGE001
Individual user is distributed in arbitrarily around it, is labeled as respectively
Figure 248725DEST_PATH_IMAGE002
, wherein
Figure 447625DEST_PATH_IMAGE001
Be positive integer,
Figure 929553DEST_PATH_IMAGE003
For Arbitrary positive integer in the scope, each user
Figure 5142DEST_PATH_IMAGE003
Send resource allocation request to eNodeB respectively;
Second step: the eNodeB learns each user according to the solicited message that receives
Figure 53738DEST_PATH_IMAGE003
Limited QoE model information, be designated as
Figure 905020DEST_PATH_IMAGE005
, wherein
Figure 616755DEST_PATH_IMAGE006
Expression is by the user
Figure 920697DEST_PATH_IMAGE003
The vector formed of the unknown parameter of QoE model;
The 3rd step: eNodeB enters the iteration renewal process, makes eNodeB finally can determine each user respectively
Figure 461400DEST_PATH_IMAGE003
The optimum stock number that can get
Figure 416455DEST_PATH_IMAGE007
With optimum length of testing speech
Figure 813939DEST_PATH_IMAGE008
, and this information is notified to each user
Figure 277281DEST_PATH_IMAGE003
, concrete iteration renewal process is as follows:
(1) in primary iteration The time, each user of eNodeB initialization
Figure 667122DEST_PATH_IMAGE003
The stock number that can get
Figure 766665DEST_PATH_IMAGE010
, length of testing speech
Figure 763309DEST_PATH_IMAGE011
, obtainable resource increment
Figure 645814DEST_PATH_IMAGE012
With the length of testing speech increment
Figure 326194DEST_PATH_IMAGE013
, wherein
Figure 268743DEST_PATH_IMAGE014
,
Figure 254147DEST_PATH_IMAGE015
Be total available volume of resources;
(2)
Figure 2661DEST_PATH_IMAGE016
During inferior iteration, clear and definite current each user of eNodeB
Figure 209651DEST_PATH_IMAGE003
The length of testing speech increment , adjust length of testing speech
Figure 831354DEST_PATH_IMAGE018
With the stock number of getting
Figure 852400DEST_PATH_IMAGE019
, and with these information notice to each user, wherein
Figure 179476DEST_PATH_IMAGE016
Be nonnegative integer;
(3) each user
Figure 11297DEST_PATH_IMAGE003
According to the information that receives, calculate at the length of testing speech increment In the stock number of getting be
Figure 779719DEST_PATH_IMAGE010
The time average MOS value
Figure 961301DEST_PATH_IMAGE020
, and this value fed back to eNodeB;
(4) eNodeB tries to achieve and satisfies equation
Figure 462559DEST_PATH_IMAGE021
Relevant
Figure 96802DEST_PATH_IMAGE022
Solution, wherein
Figure 990809DEST_PATH_IMAGE022
Expression is for the user
Figure 292477DEST_PATH_IMAGE003
The unknown parameter of QoE model
Figure 466101DEST_PATH_IMAGE016
The vector that estimated value is formed during inferior iteration;
(5) eNodeB upgrades each user
Figure 587641DEST_PATH_IMAGE003
Obtainable resource increment
Figure 957442DEST_PATH_IMAGE023
With the length of testing speech increment
Figure 175934DEST_PATH_IMAGE024
, namely
Figure 972988DEST_PATH_IMAGE025
,
Figure 158988DEST_PATH_IMAGE026
(6) eNodeB judges the resource increment that obtains With the length of testing speech increment Whether satisfy and upgrade restrictive condition, determine with final With
Figure 938408DEST_PATH_IMAGE024
The size of value, be specially: if
Figure 463062DEST_PATH_IMAGE027
Set up, then
Figure 328249DEST_PATH_IMAGE028
, and
Figure 795003DEST_PATH_IMAGE029
Otherwise, be false as if above-mentioned inequality, and
Figure 378431DEST_PATH_IMAGE030
Set up, then If above-mentioned two inequality all are false, then
Figure 659425DEST_PATH_IMAGE028
(7) eNodeB judges each user
Figure 969184DEST_PATH_IMAGE003
Whether all satisfy
Figure 367804DEST_PATH_IMAGE031
, wherein,
Figure 421211DEST_PATH_IMAGE032
Than decimal, its value is decided the requirement of convergence rate and precision according to system for predefined, if set up, and then order , enter new round iterative process; Otherwise then iteration stops, and obtains each user
Figure 289121DEST_PATH_IMAGE003
The optimum stock number that can get
Figure 909458DEST_PATH_IMAGE034
With optimum length of testing speech
The 4th step: each user
Figure 766567DEST_PATH_IMAGE003
According to the information that receives, learn the optimum stock number of getting separately
Figure 683708DEST_PATH_IMAGE007
With optimum length of testing speech
Figure 260183DEST_PATH_IMAGE008
,
Figure 655392DEST_PATH_IMAGE036
In the duration, use the optimum stock number of getting
Figure 469764DEST_PATH_IMAGE007
, wherein
Figure 105276DEST_PATH_IMAGE037
Be the user
Figure 372309DEST_PATH_IMAGE003
The streaming media playing duration.
The scope of application of the present invention is based on the LTE streaming media communication of QoE, and the resource optimization when realizing that each user QoE model information is incomplete is assigned as purpose, realizes the design of optimum dynamic resource allocation scheme by on-line testing-optimisation strategy.All known research scene is different with each user QoE model of hypothesis in the conventional method, and what the present invention considered is the situation that only has limited QoE model information, or even the reproduction time of Streaming Media is also uncertain, and this will have more practical significance.Abandon the theoretical and stochastic approximation theory of Bayesian Estimation now commonly used, both avoided the prior distribution of unknown parameter and the dependence of historical information have also been reduced computation complexity, be more conducive to the real-time online operation.Particularly, the present invention unites consideration QoE model and the uncertainty of streaming media playing time, the dynamic resource optimization that has designed based on on-line testing-optimisation strategy distributes, to determine best length of testing speech and corresponding optimal resource allocation situation, reduce the testing time in the actual flow media communication and strengthen intrinsic contradiction between the estimated accuracy thereby solved.
Description of drawings
Fig. 1 is a typical LTE network, and an eNodeB is positioned at the optional position,
Figure 899106DEST_PATH_IMAGE001
Individual user is distributed in around it arbitrarily.Each user sends its average MOS value to eNodeB, eNodeB calculates the obtainable optimum stock number of each user and optimum length of testing speech, and notifies each user under the condition of the part QoE model information that only has each user, make each user in residual time length, use the optimum stock number of getting.
Fig. 2 is the algorithm flow chart that the dynamic resource allocation method that the present invention is based on Streaming Media when transmission of QoE is determined the obtainable optimum stock number of each user and optimum length of testing speech.
Iterative computation was to determine the algorithm flow chart of optimal resource allocation when Fig. 3 each user QoE model information that to be the present invention have at eNodeB was incomplete.
Embodiment
Below in conjunction with Figure of description the invention is described in further detail.
Embodiment one
The first step: in a LTE network, there is an eNodeB who is positioned at the optional position,
Figure 567984DEST_PATH_IMAGE001
Individual user is distributed in arbitrarily around it, is labeled as respectively , wherein
Figure 361683DEST_PATH_IMAGE001
Be positive integer, For
Figure 949976DEST_PATH_IMAGE004
Arbitrary positive integer in the scope, each user
Figure 379820DEST_PATH_IMAGE003
Send resource allocation request to eNodeB respectively, the request to send signal RTS in the setting of described request signal and 802.11 in the MAC agreement, request-to-send sets similar, specifically referring to " IEEE 802.11 1999 Edition ";
Second step: the eNodeB learns each user according to the solicited message that receives
Figure 168916DEST_PATH_IMAGE003
Limited QoE model information, be designated as
Figure 444039DEST_PATH_IMAGE005
, wherein
Figure 149827DEST_PATH_IMAGE006
Expression is by the user
Figure 484994DEST_PATH_IMAGE003
The vector formed of the unknown parameter of QoE model, the QoE model is according to the difference of Streaming Media application type and different, for example, if the stock number of getting is
Figure 213915DEST_PATH_IMAGE010
The time, at audio service, the QoE model can be portrayed and be , at video traffic, the QoE model can be portrayed and be
Figure 367871DEST_PATH_IMAGE039
, wherein,
Figure 936255DEST_PATH_IMAGE040
,
Figure 886894DEST_PATH_IMAGE041
Be Packet Error Ratio;
The 3rd step: eNodeB enters the iteration renewal process, makes eNodeB finally can determine each user respectively The optimum stock number that can get With optimum length of testing speech
Figure 877481DEST_PATH_IMAGE008
, and this information is notified to each user
Figure 643311DEST_PATH_IMAGE003
, concrete iteration renewal process is as follows:
(1) in primary iteration
Figure 63928DEST_PATH_IMAGE009
The time, each user of eNodeB initialization
Figure 582503DEST_PATH_IMAGE003
The stock number that can get
Figure 695953DEST_PATH_IMAGE010
, length of testing speech
Figure 949080DEST_PATH_IMAGE011
, obtainable resource increment
Figure 173388DEST_PATH_IMAGE012
With the length of testing speech increment
Figure 234884DEST_PATH_IMAGE013
, wherein ,
Figure 479232DEST_PATH_IMAGE015
Be total available volume of resources;
(2)
Figure 569548DEST_PATH_IMAGE016
During inferior iteration, clear and definite current each user of eNodeB
Figure 751131DEST_PATH_IMAGE003
The length of testing speech increment
Figure 523826DEST_PATH_IMAGE017
, adjust length of testing speech
Figure 423649DEST_PATH_IMAGE018
With the stock number of getting
Figure 989760DEST_PATH_IMAGE019
, and with these information notice to each user, wherein
Figure 88166DEST_PATH_IMAGE016
Be nonnegative integer;
(3) each user
Figure 448740DEST_PATH_IMAGE003
According to the information that receives, calculate at the length of testing speech increment In the stock number of getting be
Figure 18710DEST_PATH_IMAGE010
The time average MOS value
Figure 237202DEST_PATH_IMAGE020
The specific implementation process can feed back to eNodeB with this value then referring to " S. Khan; S. Duhovnikov; E. Steinbach; and W. Kellerer. MOS-based multiuser multiapplication cross-layer optimization for mobile multimedia communication. Advances in Multimedia; Article ID 94918,2007 ";
(4) eNodeB tries to achieve and satisfies equation
Figure 34256DEST_PATH_IMAGE021
Relevant
Figure 689097DEST_PATH_IMAGE022
Solution, wherein Expression is for the user
Figure 873271DEST_PATH_IMAGE003
The unknown parameter of QoE model
Figure 169123DEST_PATH_IMAGE016
The vector that estimated value is formed during inferior iteration;
(5) eNodeB upgrades each user
Figure 265255DEST_PATH_IMAGE003
Obtainable resource increment With the length of testing speech increment
Figure 389517DEST_PATH_IMAGE024
, namely
Figure 590691DEST_PATH_IMAGE025
,
Figure 174120DEST_PATH_IMAGE026
(6) eNodeB judges the resource increment that obtains With the length of testing speech increment
Figure 986273DEST_PATH_IMAGE024
Whether satisfy and upgrade restrictive condition, determine with final With
Figure 163493DEST_PATH_IMAGE024
The size of value, be specially: if
Figure 216900DEST_PATH_IMAGE027
Set up, then
Figure 869729DEST_PATH_IMAGE028
, and
Figure 350389DEST_PATH_IMAGE029
Otherwise, be false as if above-mentioned inequality, and Set up, then
Figure 827824DEST_PATH_IMAGE029
If above-mentioned two inequality all are false, then
Figure 522110DEST_PATH_IMAGE028
(7) eNodeB judges each user
Figure 219677DEST_PATH_IMAGE003
Whether all satisfy
Figure 264993DEST_PATH_IMAGE031
, wherein,
Figure 988098DEST_PATH_IMAGE032
Than decimal, its value is decided the requirement of convergence rate and precision according to system for predefined, if set up, and then order , enter new round iterative process; Otherwise then iteration stops, and obtains each user
Figure 172403DEST_PATH_IMAGE003
The optimum stock number that can get
Figure 439436DEST_PATH_IMAGE034
With optimum length of testing speech
Figure 700654DEST_PATH_IMAGE035
The 4th step: each user
Figure 103953DEST_PATH_IMAGE003
According to the information that receives, learn the optimum stock number of getting separately With optimum length of testing speech
Figure 711967DEST_PATH_IMAGE008
,
Figure 448979DEST_PATH_IMAGE036
In the duration, use the optimum stock number of getting
Figure 769102DEST_PATH_IMAGE007
, wherein
Figure 198946DEST_PATH_IMAGE037
Be the user
Figure 988042DEST_PATH_IMAGE003
The streaming media playing duration.
 
Provide concrete example below in conjunction with Figure of description:
Consider a LTE network, have an eNodeB who is positioned at the optional position, 5 users are distributed in arbitrarily around it, are labeled as respectively , total available volume of resources
Figure 500112DEST_PATH_IMAGE015
Be normalized to 1, have Voice ﹠ Video two class streaming media service, with the user Be example, if the stock number that it is got is
Figure 564200DEST_PATH_IMAGE010
, then the QoE model of its corresponding above-mentioned two class streaming media service is respectively
Figure 689019DEST_PATH_IMAGE038
With
Figure 186997DEST_PATH_IMAGE039
, wherein,
Figure 20961DEST_PATH_IMAGE040
, and component With
Figure 667154DEST_PATH_IMAGE044
For eNodeB for the user
Figure 19638DEST_PATH_IMAGE003
The unknown parameter of QoE model, Packet Error Ratio
Figure 962186DEST_PATH_IMAGE041
For
Figure 462437DEST_PATH_IMAGE045
, in addition, for simplicity, make each user
Figure 883054DEST_PATH_IMAGE003
The streaming media playing duration
Figure 932788DEST_PATH_IMAGE037
All be normalized to 1.
As Fig. 2 and Fig. 3, the implementation procedure of whole example is as follows:
The first step: each user Send resource allocation request to eNodeB respectively;
Second step: the eNodeB learns each user according to the solicited message that receives Limited QoE model information;
The 3rd step: eNodeB enters the iteration renewal process, makes eNodeB finally can determine each user respectively
Figure 726934DEST_PATH_IMAGE003
The optimum stock number that can get
Figure 867060DEST_PATH_IMAGE007
With optimum length of testing speech
Figure 151411DEST_PATH_IMAGE008
, and this information is notified to each user , concrete iteration renewal process is as follows:
(1) in primary iteration
Figure 654253DEST_PATH_IMAGE009
The time, each user of eNodeB initialization
Figure 835836DEST_PATH_IMAGE003
The stock number that can get
Figure 337093DEST_PATH_IMAGE046
, length of testing speech , obtainable resource increment
Figure 130923DEST_PATH_IMAGE048
With the length of testing speech increment
Figure 167012DEST_PATH_IMAGE049
(2)
Figure 793165DEST_PATH_IMAGE016
During inferior iteration, clear and definite current each user of eNodeB
Figure 727754DEST_PATH_IMAGE003
The length of testing speech increment
Figure 831977DEST_PATH_IMAGE017
, adjust length of testing speech With the stock number of getting
Figure 847523DEST_PATH_IMAGE019
, and with these information notice to each user, wherein
Figure 39382DEST_PATH_IMAGE016
Be nonnegative integer;
(3) each user According to the information that receives, calculate at the length of testing speech increment
Figure 223556DEST_PATH_IMAGE017
In the stock number of getting be
Figure 988249DEST_PATH_IMAGE010
The time average MOS value, and this value fed back to eNodeB;
(4) eNodeB obtains according to this for the user The unknown parameter of QoE model
Figure 343456DEST_PATH_IMAGE016
Estimated value during inferior iteration;
(5) eNodeB upgrades each user Obtainable resource increment
Figure 940976DEST_PATH_IMAGE023
With the length of testing speech increment
(6) eNodeB judges the resource increment that obtains
Figure 774120DEST_PATH_IMAGE023
With the length of testing speech increment
Figure 805399DEST_PATH_IMAGE024
Whether satisfy and upgrade restrictive condition, determine with final
Figure 115157DEST_PATH_IMAGE023
With
Figure 513778DEST_PATH_IMAGE024
The size of value;
(7) eNodeB judges each user Whether all satisfy
Figure 954434DEST_PATH_IMAGE050
, if set up, then order
Figure 435094DEST_PATH_IMAGE033
, enter new round iterative process; Otherwise then iteration stops, and obtains each user
Figure 789852DEST_PATH_IMAGE003
The optimum stock number that can get
Figure 646950DEST_PATH_IMAGE034
With optimum length of testing speech
Figure 341236DEST_PATH_IMAGE035
The 4th step: each user
Figure 569961DEST_PATH_IMAGE003
According to the information that receives, learn the optimum stock number of getting separately
Figure 615278DEST_PATH_IMAGE007
With optimum length of testing speech
Figure 807225DEST_PATH_IMAGE008
,
Figure 356018DEST_PATH_IMAGE036
In the duration, use the optimum stock number of getting
Figure 257109DEST_PATH_IMAGE007

Claims (1)

1. one kind based on the optimal resource allocation method in the LTE streaming media communication of QoE, it is characterized in that:
The first step: in a LTE network, there is an eNodeB who is positioned at the optional position,
Figure 525608DEST_PATH_IMAGE002
Individual user is distributed in arbitrarily around it, is labeled as respectively
Figure 74401DEST_PATH_IMAGE004
, wherein
Figure 733747DEST_PATH_IMAGE002
Be positive integer,
Figure 781DEST_PATH_IMAGE006
For
Figure 730839DEST_PATH_IMAGE008
Arbitrary positive integer in the scope, each user
Figure 399718DEST_PATH_IMAGE006
Send resource allocation request to eNodeB respectively;
Second step: the eNodeB learns each user according to the solicited message that receives Limited QoE model information, be designated as
Figure 960460DEST_PATH_IMAGE010
, wherein
Figure 963052DEST_PATH_IMAGE012
Expression is by the user
Figure 814333DEST_PATH_IMAGE006
The vector formed of the unknown parameter of QoE model;
The 3rd step: eNodeB enters the iteration renewal process, makes eNodeB finally can determine each user respectively
Figure 978598DEST_PATH_IMAGE006
The optimum stock number that can get With optimum length of testing speech
Figure 806931DEST_PATH_IMAGE016
, and this information is notified to each user
Figure 184823DEST_PATH_IMAGE006
, concrete iteration renewal process is as follows:
In primary iteration The time, each user of eNodeB initialization
Figure 576807DEST_PATH_IMAGE006
The stock number that can get
Figure 468671DEST_PATH_IMAGE020
, length of testing speech
Figure 966649DEST_PATH_IMAGE022
, obtainable resource increment With the length of testing speech increment , wherein ,
Figure 563404DEST_PATH_IMAGE030
Be total available volume of resources;
Figure 240373DEST_PATH_IMAGE032
During inferior iteration, clear and definite current each user of eNodeB
Figure 475045DEST_PATH_IMAGE006
The length of testing speech increment
Figure 161241DEST_PATH_IMAGE034
, adjust length of testing speech
Figure 181281DEST_PATH_IMAGE036
With the stock number of getting
Figure 294731DEST_PATH_IMAGE038
, and with these information notice to each user, wherein
Figure 547857DEST_PATH_IMAGE032
Be nonnegative integer;
Each user
Figure 772165DEST_PATH_IMAGE006
According to the information that receives, calculate at the length of testing speech increment
Figure 99241DEST_PATH_IMAGE034
In the stock number of getting be The time average MOS value
Figure 576545DEST_PATH_IMAGE040
, and this value fed back to eNodeB;
ENodeB tries to achieve and satisfies equation
Figure 932440DEST_PATH_IMAGE042
Relevant
Figure 114023DEST_PATH_IMAGE044
Solution, wherein
Figure 569275DEST_PATH_IMAGE044
Expression is for the user
Figure 16568DEST_PATH_IMAGE006
The unknown parameter of QoE model
Figure 582678DEST_PATH_IMAGE032
The vector that estimated value is formed during inferior iteration;
ENodeB upgrades each user
Figure 681084DEST_PATH_IMAGE006
Obtainable resource increment
Figure 307238DEST_PATH_IMAGE046
With the length of testing speech increment
Figure 428778DEST_PATH_IMAGE048
, namely ,
Figure 537777DEST_PATH_IMAGE052
ENodeB judges the resource increment that obtains
Figure 131569DEST_PATH_IMAGE046
With the length of testing speech increment
Figure 740405DEST_PATH_IMAGE048
Whether satisfy and upgrade restrictive condition, determine with final
Figure 461368DEST_PATH_IMAGE046
With
Figure 472049DEST_PATH_IMAGE048
The size of value, be specially: if
Figure 440005DEST_PATH_IMAGE054
Set up, then
Figure 864033DEST_PATH_IMAGE056
, and
Figure 575637DEST_PATH_IMAGE058
Otherwise, be false as if above-mentioned inequality, and
Figure 752409DEST_PATH_IMAGE060
Set up, then
Figure 891267DEST_PATH_IMAGE058
If above-mentioned two inequality all are false, then
Figure 209116DEST_PATH_IMAGE056
ENodeB judges each user
Figure 52307DEST_PATH_IMAGE006
Whether all satisfy
Figure 37580DEST_PATH_IMAGE062
, wherein,
Figure 894809DEST_PATH_IMAGE064
Than decimal, its value is decided the requirement of convergence rate and precision according to system for predefined, if set up, and then order
Figure 699954DEST_PATH_IMAGE066
, enter new round iterative process; Otherwise then iteration stops, and obtains each user
Figure 753361DEST_PATH_IMAGE006
The optimum stock number that can get
Figure 655457DEST_PATH_IMAGE068
With optimum length of testing speech
Figure 713281DEST_PATH_IMAGE070
The 4th step: each user
Figure 271301DEST_PATH_IMAGE006
According to the information that receives, learn the optimum stock number of getting separately
Figure 862820DEST_PATH_IMAGE014
With optimum length of testing speech
Figure 619423DEST_PATH_IMAGE016
,
Figure 270984DEST_PATH_IMAGE072
In the duration, use the optimum stock number of getting
Figure 863771DEST_PATH_IMAGE014
, wherein
Figure 258980DEST_PATH_IMAGE074
Be the user
Figure 73352DEST_PATH_IMAGE006
The streaming media playing duration.
CN201310058495.9A 2013-02-25 2013-02-25 Based on the optimal resource allocation method in the LTE streaming media communication of QoE Expired - Fee Related CN103200592B (en)

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