CN103200592B - Based on the optimal resource allocation method in the LTE streaming media communication of QoE - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 19
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Abstract
Based on the optimal resource allocation method in the LTE streaming media communication of QoE, by the Dynamic Resource Allocation for Multimedia of design based on on-line testing-optimisation strategy, to realize the resources configuration optimization when eNodeB is incomplete to each user QoE model information.Especially, consider QoE model and the uncertainty of streaming media playing time owing to combining, thus obtain optimal compromise on measuring accuracy and Optimal performance.
Description
Technical field
What the present invention relates to is a kind of for based on the optimal resource allocation method in the LTE streaming media communication of QoE, specifically a kind ofly consider QoE model and the uncertain situation of streaming media playing time combining, by the Dynamic Resource Allocation for Multimedia of design based on on-line testing-optimisation strategy, to realize the resource optimal distribution method obtaining optimal compromise on measuring accuracy and Optimal performance.
Background technology
That studies along with the universal of broadband network and LTE deepens continuously, stream media technology obtains development at full speed, increasingly become one of the most popular business in LTE network, and the application system of Streaming Media, international standard and basic research also become the focus of current industry and scientific research close attention.But notably, Stream Media Application popularizing in every field be realized, the service quality of Stream Media Application need be improved.In order to reach this target, Internet resources are reasonably distributed and manage essential; Otherwise when application constantly increases, network performance significantly will decline along with the minimizing of Internet resources.As can be seen here, the optimization of Internet resources distributes the key being not only network stabilization, Effec-tive Function, also be the basis and the prerequisite that realize various service quality simultaneously, particularly all than tradition, much bigger Stream Media Application is applied to the demand rate of network, duration, seem more most important.In addition, consider that actual LTE network is the system of development change, the research distributed the optimization of its resource will be one and have challenging research field.
Tradition resolving ideas mainly uses network QoS (QualityofService) parameter to describe corresponding service quality, reasonably configures to realize resource high-efficiency.Specifically, QoS primary responsibility carries out service management from the angle of network and provides the otherness of business, and network entity processes different business according to different quality requirements.Unfortunately, because streaming media is comparatively responsive to time delay, to packet loss and bit error rate requirement comparatively harsh, add the variation of network access mode and the isomerism of the network terminal, make original network QoS framework need take into full account the experience of end-to-end user, particularly for the performance index of different classes of business, more feasible mode is defined from the angle of user and is described.So, QoE(QualityofExperience) framework arises at the historic moment.It is defined as the appreciable service quality of user, and namely terminal use is to the subjective feeling of the communication service performance that network provides.Because it is more accurate in description user's request, so can accurate definition stream media service system optimization aim.
In fact, the resources configuration optimization research based on QoE in streaming media communication obtains extensive concern, but great majority research all supposes that the QoE model of each user is known for system.But most of actual conditions are that such as, user side by side selects polytype streaming media service arbitrarily about the information of QoE model is incomplete; Multimedia application in DYNAMIC COMPLEX environment etc.In such cases, be difficult to sometimes even not obtain complete QoE model information in advance.In addition, due to the particularity of streaming media communication, its reproduction time is usual not known yet, and this has deepened the difficulty that resource allocation problem solves further.
To this kind of problem, existing resolving ideas is mainly divided into two classes, i.e. Bayesian Estimation theory and stochastic approximation theory.The former inferior position is to depend on the prior distribution to unknown parameter to a great extent, and this is still unfeasible for actual flow media communication.And the latter need borrow power in a large amount of historical information, and its computation complexity is higher, is unsuitable for real-time online operation.In addition, consider the particularity of streaming media communication, such as its reproduction time is also unknown, therefore more cannot ensure that a large amount of real data can be observed.
Given this, for QoE model information in streaming media communication not exclusively under Resourse Distribute, many problem demanding prompt solutions will be there is: within the streaming media playing time, does is it feasible for spending some times to test QoE model? how to design suitable on-line testing-optimisation strategy solve the reduction testing time and strengthen contradiction intrinsic between estimated accuracy? in actual multimedia communications system, how does this implement this strategy again? in one word, the existence of these problems seriously constrains the raising of the service quality of Stream Media Application, needs to be researched and solved further.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, propose and be a kind ofly applicable to based on the optimal resource allocation method in the LTE streaming media communication of QoE, the present invention can combine consideration QoE model and the uncertainty of streaming media playing time, thus obtains optimal compromise on measuring accuracy and Optimal performance.By the Dynamic Resource Allocation for Multimedia of design based on on-line testing-optimisation strategy, to realize the resources configuration optimization when eNodeB is incomplete to each user QoE model information.
The present invention is achieved by the following technical solutions:
, there is the eNodeB that is positioned at optional position in the first step: in a LTE network,
individual user's Arbitrary distribution, around it, is labeled as 1,2 respectively ..., n ..., N, wherein
for positive integer,
for
arbitrary positive integer in scope, each user
resource allocation request is sent respectively to eNodeB;
Second step: eNodeB, according to the solicited message received, learns each user
limited QoE model information, be designated as
, wherein
represent by user
the vector that forms of the unknown parameter of QoE model;
3rd step: eNodeB enters iteration renewal process, makes eNodeB finally can determine each user respectively
the optimum stock number that can get
with optimum length of testing speech
, and give each user by this message notice
, concrete iteration renewal process is as follows:
(1) in primary iteration
time, each user of eNodeB initialization
the stock number that can get
, length of testing speech
, obtainable resource increment
with length of testing speech increment
, wherein
,
for total available volume of resources;
(2)
during secondary iteration, the clearly current each user of eNodeB
length of testing speech increment
, adjustment length of testing speech
with the stock number of getting
, and by these message notice to each user, wherein
for nonnegative integer;
(3) each user
according to the information received, calculate at length of testing speech increment
the stock number of inside getting is
time average MOS value
, and this value is fed back to eNodeB;
(4) eNodeB tries to achieve and meets equation
relevant
solution, wherein
represent for user
the unknown parameter of QoE model
the vector of value composition estimated during secondary iteration;
(5) eNodeB upgrades each user
obtainable resource increment
with length of testing speech increment
, namely
,
;
(6) eNodeB judges the resource increment that obtains
with length of testing speech increment
whether meet and upgrade restrictive condition, finally to determine
with
the size of value, be specially: if
set up, then
, and
; Otherwise, if above-mentioned inequality is false, and
set up, then
; If above-mentioned two inequality are all false, then
;
(7) eNodeB judges each user
whether all satisfied
, wherein,
for the comparatively decimal preset, its value determine the requirement of convergence rate and precision according to system, if establishment, then makes
, enter new round iterative process; Otherwise, then iteration ends, and obtain each user
the optimum stock number that can get
with optimum length of testing speech
;
4th step: each user
according to the information received, learn the optimum stock number of getting separately
with optimum length of testing speech
,
in duration, use the optimum stock number of getting
, wherein
for user
streaming media playing duration.
The scope of application of the present invention is the LTE streaming media communication based on QoE, by realize each user QoE model information incomplete time resources configuration optimization for the purpose of, realized the design of optimum dynamic resource allocation scheme by on-line testing-optimisation strategy.The research scene all known from supposing each user QoE model in conventional method is different, and the present invention is it is considered that only have the situation of limited QoE model information, or even the reproduction time of Streaming Media is also uncertain, and this will have more practical significance.Abandon that now conventional Bayesian Estimation is theoretical and stochastic approximation is theoretical, both avoided the dependence of prior distribution to unknown parameter and historical information, and also reduced computation complexity, be more conducive to real-time online and operate.Specifically, the present invention combines consideration QoE model and the uncertainty of streaming media playing time, the dynamic resource optimization devised based on on-line testing-optimisation strategy distributes, to determine best length of testing speech and corresponding optimal resource allocation situation, thus solve in actual flow media communication and reduce the testing time and strengthen contradiction intrinsic between estimated accuracy.
Accompanying drawing explanation
Fig. 1 is a typical LTE network, and an eNodeB is positioned at optional position,
individual user's Arbitrary distribution is around it.Each user sends its average MOS value to eNodeB, eNodeB, under the condition of part QoE model information only having each user, calculates the obtainable optimum stock number of each user and optimum length of testing speech, and notifies each user, make each user in residual time length, use the optimum stock number of getting.
Dynamic resource allocation method when Fig. 2 is the streaming media that the present invention is based on QoE is to determine the algorithm flow chart of the obtainable optimum stock number of each user and optimum length of testing speech.
Fig. 3 be the present invention when each user QoE model information that eNodeB has is incomplete iterative computation to determine the algorithm flow chart of optimal resource allocation.
Embodiment
Below in conjunction with Figure of description, the invention is described in further detail.
embodiment one
, there is the eNodeB that is positioned at optional position in the first step: in a LTE network,
individual user's Arbitrary distribution, around it, is labeled as 1,2 respectively ..., n ..., N, wherein
for positive integer,
for
arbitrary positive integer in scope, each user
send resource allocation request respectively to eNodeB, the request to send signal RTS in the setting of described request signal and 802.11 in MAC protocol, request-to-send setting is similar, specifically see " IEEE802.111999Edition ";
Second step: eNodeB, according to the solicited message received, learns each user
limited QoE model information, be designated as
, wherein
represent by user
the vector that forms of the unknown parameter of QoE model, QoE model is different according to the difference of Stream Media Application type, such as, if the stock number of getting is
time, for audio service, QoE model can portray into
, for video traffic, QoE model can portray into
, wherein,
,
for Packet Error Ratio;
3rd step: eNodeB enters iteration renewal process, makes eNodeB finally can determine each user respectively
the optimum stock number that can get
with optimum length of testing speech
, and give each user by this message notice
, concrete iteration renewal process is as follows:
(1) in primary iteration
time, each user of eNodeB initialization
the stock number that can get
, length of testing speech
, obtainable resource increment
with length of testing speech increment
, wherein
,
for total available volume of resources;
(2)
during secondary iteration, the clearly current each user of eNodeB
length of testing speech increment
, adjustment length of testing speech
with the stock number of getting
, and by these message notice to each user, wherein
for nonnegative integer;
(3) each user
according to the information received, calculate at length of testing speech increment
the stock number of inside getting is
time average MOS value
this value see " S.Khan; S.Duhovnikov; E.Steinbach; andW.Kellerer.MOS-basedmultiusermultiapplicationcross-la yeroptimizationformobilemultimediacommunication.Advances inMultimedia; ArticleID94918,2007 ", can be fed back to eNodeB by specific implementation process then;
(4) eNodeB tries to achieve and meets equation
relevant
solution, wherein
represent for user
the unknown parameter of QoE model
the vector of value composition estimated during secondary iteration;
(5) eNodeB upgrades each user
obtainable resource increment
with length of testing speech increment
, namely
,
;
(6) eNodeB judges the resource increment that obtains
with length of testing speech increment
whether meet and upgrade restrictive condition, finally to determine
with
the size of value, be specially: if
set up, then
, and
; Otherwise, if above-mentioned inequality is false, and
set up, then
; If above-mentioned two inequality are all false, then
;
(7) eNodeB judges each user
whether all satisfied
, wherein,
for the comparatively decimal preset, its value determine the requirement of convergence rate and precision according to system, if establishment, then makes
, enter new round iterative process; Otherwise, then iteration ends, and obtain each user
the optimum stock number that can get
with optimum length of testing speech
;
4th step: each user
according to the information received, learn the optimum stock number of getting separately
with optimum length of testing speech
,
in duration, use the optimum stock number of getting
, wherein
for user
streaming media playing duration.
Concrete example is provided below in conjunction with Figure of description:
Consider a LTE network, there is the eNodeB that is positioned at optional position, 5 user's Arbitrary distribution, around it, are labeled as respectively
, total available volume of resources
be normalized to 1, there is Voice & Video two class streaming media service, with user
for example, if the stock number that it is got is
, then the QoE model of the above-mentioned two class streaming media service corresponding to it is respectively
with
, wherein,
, and component
with
for eNodeB is for user
the unknown parameter of QoE model, Packet Error Ratio
for
, in addition, for simplicity, make each user
streaming media playing duration
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
resource allocation request is sent respectively to eNodeB;
Second step: eNodeB, according to the solicited message received, learns each user
limited QoE model information;
3rd step: eNodeB enters iteration renewal process, makes eNodeB finally can determine each user respectively
the optimum stock number that can get
with optimum length of testing speech
, and give each user by this message notice
, concrete iteration renewal process is as follows:
(1) in primary iteration
time, each user of eNodeB initialization
the stock number that can get
, length of testing speech
, obtainable resource increment
with length of testing speech increment
;
(2)
during secondary iteration, the clearly current each user of eNodeB
length of testing speech increment
, adjustment length of testing speech
with the stock number of getting
, and by these message notice to each user, wherein
for nonnegative integer;
(3) each user
according to the information received, calculate at length of testing speech increment
the stock number of inside getting is
time average MOS value, and this value is fed back to eNodeB;
(4) eNodeB obtains according to this for user
the unknown parameter of QoE model
value estimated during secondary iteration;
(5) eNodeB upgrades each user
obtainable resource increment
with length of testing speech increment
;
(6) eNodeB judges the resource increment that obtains
with length of testing speech increment
whether meet and upgrade restrictive condition, finally to determine
with
the size of value;
(7) eNodeB judges each user
whether all satisfied
if set up, then make
, enter new round iterative process; Otherwise, then iteration ends, and obtain each user
the optimum stock number that can get
with optimum length of testing speech
;
4th step: each user
according to the information received, learn the optimum stock number of getting separately
with optimum length of testing speech
,
in duration, use the optimum stock number of getting
.
Claims (1)
1., based on the optimal resource allocation method in the LTE streaming media communication of QoE, it is characterized in that:
, there is the eNodeB that is positioned at optional position in the first step: in a LTE network,
individual user's Arbitrary distribution, around it, is labeled as 1,2 respectively ..., n ..., N, wherein
for positive integer,
for
arbitrary positive integer in scope, each user
resource allocation request is sent respectively to eNodeB;
Second step: eNodeB, according to the solicited message received, learns each user
limited QoE model information, be designated as
, wherein
represent by user
the vector that forms of the unknown parameter of QoE model; If the stock number of getting is
time, for audio service, QoE model is
, for video traffic, QoE model is
, wherein,
,
for Packet Error Ratio;
3rd step: eNodeB enters iteration renewal process, makes eNodeB finally can determine each user respectively
the optimum stock number that can get
with optimum length of testing speech
, and give each user by this message notice
, concrete iteration renewal process is as follows:
In primary iteration
time, each user of eNodeB initialization
the stock number that can get
, length of testing speech
, obtainable resource increment
with length of testing speech increment
, wherein
,
for total available volume of resources;
?
during secondary iteration, the clearly current each user of eNodeB
length of testing speech increment
, adjustment length of testing speech
with the stock number of getting
, and by these message notice to each user, wherein
for nonnegative integer;
Each user
according to the information received, calculate at length of testing speech increment
the stock number of inside getting is
time average MOS value
, and this value is fed back to eNodeB;
ENodeB tries to achieve and meets equation
relevant
solution, wherein
represent for user
the unknown parameter of QoE model
the vector of value composition estimated during secondary iteration;
ENodeB upgrades each user
obtainable resource increment
with length of testing speech increment
, namely
,
;
ENodeB judges the resource increment obtained
with length of testing speech increment
whether meet and upgrade restrictive condition, finally to determine
with
the size of value, be specially: if
set up, then
, and
; Otherwise, if above-mentioned inequality is false, and
set up, then
; If above-mentioned two inequality are all false, then
;
ENodeB judges each user
whether all satisfied
, wherein,
for the comparatively decimal preset, its value determine the requirement of convergence rate and precision according to system, if establishment, then makes
, enter new round iterative process; Otherwise, then iteration ends, and obtain each user
the optimum stock number that can get
with optimum length of testing speech
;
4th step: each user
according to the information received, learn the optimum stock number of getting separately
with optimum length of testing speech
,
in duration, use the optimum stock number of getting
, wherein
for user
streaming media playing duration.
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| CN109597687B (en) * | 2018-10-31 | 2020-11-13 | 东软集团股份有限公司 | Resource allocation method and device for data synchronization, storage medium and electronic equipment |
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| QoE-driven Live and On-demand LTE Uplink Video Transmission;Ali E1 Essaili, et al,;《MMSP 2011》;20111231;全文 * |
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