阅读说明：本技术 一种基于合作博弈的移动边缘场景中的视频码率决策方法 (Cooperative game-based video code rate decision method in mobile marginal scene ) 是由 谭小彬 李思敏 徐磊 王顺义 杨坚 郑烇 于 2020-07-21 设计创作，主要内容包括：本发明提出一种基于合作博弈的移动边缘场景中的视频码率决策方法,包括：步骤1、移动用户将各自的信道质量及缓冲区状态报告给各自连接的基站,而基站继续将这些信息上报给边缘服务器,进而边缘服务器依据这些信息将用户分为多个组播组；步骤2、组播组作为一个整体参与组播组间的合作博弈,组播服务器根据合作博弈解进行带宽资源的分配；组播要求组内组播相同的内容,故而组内码率决策一致,通过合作博弈达成各组间资源分配和码率决策的帕累托最优解,即在不降低其他组效用的情况下,不能增加本组的效用；步骤3、组播服务器完成区域内的视频内容分发任务,并判断视频内容是否分发完毕,若已完成全部内容的分发,则结束,若还未完成,则回到步骤1继续循环。(The invention provides a video code rate decision method in a mobile marginal scene based on cooperative game, which comprises the following steps: step 1, the mobile users report the respective channel quality and the buffer area state to the respective connected base stations, the base stations continuously report the information to the edge server, and the edge server divides the users into a plurality of multicast groups according to the information; step 2, the multicast group is used as a whole to participate in a cooperation game among the multicast groups, and the multicast server distributes bandwidth resources according to the cooperation game solution; the multicast requires the same content to be multicast in the group, so that the code rate decision in the group is consistent, and the pareto optimal solution of resource allocation and code rate decision among groups is achieved through cooperative game, namely the utility of the group cannot be increased under the condition of not reducing the utility of other groups; and 3, completing the distribution task of the video content in the area by the multicast server, judging whether the distribution of the video content is finished or not, finishing the distribution if all the content is distributed, and returning to the step 1 to continue circulation if the distribution is not finished.)

1. A video code rate decision method in a mobile marginal scene based on cooperative game is characterized by comprising the following steps:

step 1, the mobile users report the respective channel quality and the buffer area state to the respective connected base stations, the base stations continuously report the information to the edge server, and the edge server divides the users into a plurality of multicast groups according to the information;

step 2, the multicast group is used as a whole to participate in a cooperation game among the multicast groups, and the multicast server distributes bandwidth resources according to the cooperation game solution; the multicast requires the same content to be multicast in the group, so that the code rate decision in the group is consistent, and the pareto optimal solution of resource allocation and code rate decision among groups is achieved through cooperative game, namely the utility of the group cannot be increased under the condition of not reducing the utility of other groups;

and 3, completing the distribution task of the video content in the area by the multicast server, judging whether the distribution of the video content is finished or not, finishing the distribution if all the content is distributed, and returning to the step 1 to continue to circulate the process if the distribution is not finished.

2. The method for video rate decision in a mobile edge scene based on cooperative game as claimed in claim 1, wherein:

The mobile edge network scene applicable to the method is as follows: an edge server and a multicast server are deployed at an exchanger, the server has computing capacity and caching capacity and is connected with a plurality of base stations in the MBSFN range of a multimedia broadcast single frequency network under the management of the exchanger, the multicast single frequency network comprises one or more multicast groups which can effectively eliminate the transmission interference of adjacent MBSFN cells, a plurality of wireless devices are connected under the base stations, the exchanger is also connected with other exchangers, the edge server can carry out cost communication with the edge server in other areas, finally, the edge server is connected with a source server for storing video resources, and the edge server receives video data through the connection with the source server.

3. The method for video rate decision in a mobile edge scene based on cooperative game as claimed in claim 2, wherein:

the multicast server collects the multicast of the wireless link quality and content of the users in the area, the edge server is responsible for the calculation tasks of user grouping, wireless resource allocation and code rate decision, in addition, a cache module is arranged on the edge server, the cache and replacement of popular content are determined according to the video popularity and a cache replacement strategy, when the existing content is inquired in the group, the cache is directly provided for the group, and in addition, the cached content can be provided for the peripheral edge servers.

4. The method for video rate decision in a mobile edge scene based on cooperative game as claimed in claim 1, wherein: in step 1, the edge server divides the users into a plurality of multicast groups according to the information, and specifically includes:

step 1.1, calculate grouping information

The influence of CQI and a client buffer is considered together during grouping, for the channel quality, the channel quality and the historical state information of the channel quality are considered to represent the influence of the change of the channel quality and the channel quality state, the two factors are respectively represented by CQI and CQS, and the formula of the CQS is shown as (1), wherein a is a parameter, and the CQI is a parameter_t、CQI_t-1Indicating the CQI of the current time and the CQI of the previous time respectively; the client buffer status takes into account two pieces of information: the first is the buffer cache size, expressed by BT, which represents the time length of the buffer cache segment in seconds; second, the average code rate of buffer is expressed by BR, and with Mbps as unit, a user is set to buffer K segments, l_kAnd (3) caching the code rate of the kth fragment, wherein K is more than or equal to 1 and less than or equal to K, and the time interval of each fragment is delta T, then the BR calculation formula is shown as (2):

step 1.2, clustering

For the N users, the N users need to be divided into M multicast groups, M is set by an edge server, each cycle needs to be grouped again, and the information of each user is a four-dimensional vector f _i，f_i＝(CQI_i，CQS_i，BT_i，BR_i) I is a user serial number; the input to the clustering algorithm is a state information dataset [ f ] for a given N users₁，f₂，...，f_n]They are divided into M groups using a clustering algorithm.

5. The method for video rate decision in a mobile edge scene based on cooperative game as claimed in claim 1, wherein: in the step 2, after the grouping is completed, the edge server is used as a coordination agent of the M groups, the tasks of resource allocation and code rate decision are completed through the calculation of the cooperative game among the M groups, and the multicast server adopts a Nash bargaining model to solve the problem of the cooperative game solution for the multicast content of each group.

6. The method for video rate decision in a mobile edge scene based on cooperative game as claimed in claim 5, wherein:

the user set is [1, 2.. N ]]The multicast group is [1, 2., M ]](ii) a One multicast server has a size of B_tThe bandwidth size is time-variable, and the bandwidths are jointly allocated to the M groups; the step 2 specifically comprises the following steps:

and 2.1, calculating the user utility, wherein the user utility is equal to the user experience quality qoe, as shown in formula (3). Utility u for ith user to download kth fragment _i(k) Indicating that the utility consists of video clip quality, quality switching, and playback interruption; q. q.s_i(k) Bitrate when downloading kth segment for user i, and size (q)_i(k) Is the storage space size of the k-th fragment, x_iThe bandwidth allocated for the user i is,_kfor the buffer status before the kth segment download, in seconds, a₁，a₂，a₃R are all parameters greater than 0, a₁，a₂，a₃Representing the proportion of the video clip quality, the quality switching and the playing interruption in the utility of the user, wherein r is 0.6;

u_i(k)＝qoe_i(k) (3)

step 2.2, multicast group utility calculation

Multicast group effect, i.e. utility function u of the multicast group_m，x_mIndicating the bandwidth occupied by the mth group,

step 2.3, constructing a Nash bargaining model;

and 2.4, solving the KKT condition to obtain a resource scheduling and code rate decision method.

7. The method for video rate decision in a mobile edge scene based on cooperative game as claimed in claim 6, wherein: the step 2.3 of constructing the Nash bargaining model is as follows:

To solve the bandwidth allocation and rate decision problem, it is modeled as a Nash bargaining problem, orderFor the possible protocol set space, x_mThe bandwidth allocated by the protocol for the group m, and the bandwidth set, u, of all the groups allocated by the protocol_m(x_m) Space-based for users

the utility set defining the bifurcation point is shown in equation (9):

nash bargained outcomeThe following 4 axioms must be satisfied to become an effective bargaining result:

1) pareto optimal;

2) symmetry;

3) invariance to equivalent utility representation;

4) irrelevance to alternative independence;

there is an independent solution that satisfies the four axioms above

u_m≥y_m，m＝1，2，...，M。 (13)。

8. the method for video rate decision in a mobile edge scene based on cooperative game as claimed in claim 6, wherein: the step 2.4 of solving the KKT condition to obtain the resource scheduling and code rate decision method specifically includes:

solving the Nash bargain solving problem by using a Lagrange multiplier method, converting the optimization problem shown in (10) into a minimized logarithm sum problem shown in a formula (14) by means of a logarithm form of a continuous product, and keeping the other limiting conditions as (11), (12) and (13); then let the Lagrange multiplier be lambda, mu_mM is more than or equal to 1 and less than or equal to M, an optimization target (14) and limiting conditions (11) and (13) are constructed into a Lagrangian function L by using a Lagrangian multiplier method, as shown in a formula (15), a KKT condition solved by the problem is (16a-g), (16a) represents that the gradient is 0 when the Lagrangian function is in an extremum, and is a necessary condition for solving the optimization problem, (16b-c) is an inequality constraint condition converted from (11) and (13), (16d-e) is a Lagrangian coefficient constraint condition, a Lagrangian coefficient is not negative, (16f) is a constraint condition shown in (12), and (16g) is a relaxation complementary constraint condition of Lagrangian, and finally the Lagrangian function and the KKT condition are solved, namely a Nash solution is obtained;

y_m-u_m≤0，m＝1，2，...，M (16c)

λ≥0 (16d)

μ_m≥0 (16e)

μ_m(y_m-u_m)＝0，m＝1，2，...，M。 (16g)。

Technical Field

The invention relates to the technical field of computer networks, in particular to a video code rate decision method in a mobile edge scene based on cooperative game.

Background

Mobile Edge Computing (MEC) is a recently emerging solution that sinks the computing and storage capabilities of the network to the user side, i.e., at the edge of the network, to provide low-latency, highly reliable, large-bandwidth network services. Particularly, the user side reduces repeated access to core network resources through the bottom layer cache, and can also effectively reduce network congestion and access delay. The demand of network edge on video traffic is increasing day by day, and how to effectively utilize the scene of the edge network to construct a self-adaptive video architecture becomes a valuable issue.

Currently, the common methods for selecting the adaptive video code rate include the following methods:

in the early years, research has provided a pure client-based adaptive video algorithm, and the method is a method for the client to autonomously select video code rate according to different algorithms in a common network scene. For example, a method that a client determines an adaptive code rate according to the occupancy level of a local buffer, a method that self-adaptive selection is performed through bandwidth prediction, a SQUAD framework that comprehensively considers the occupancy level of the buffer and the bandwidth condition, and a self-adaptive algorithm based on a non-cooperative game can optimally allocate the limited server output bandwidth to a user. However, the client-only approach may result in "selfish" behavior, may produce unfair results when competing for resources in a multi-user state, and may result in insufficient utilization of network resources due to lack of coordination.

Therefore, research has been carried out to provide an adaptive video streaming method based on cooperative game, which improves the quality of video service by coordinating user behaviors. The research forms a game through alliances, divides users into different alliances, and further achieves the pareto optimal solution of network resource allocation through redistribution of surplus interests of the alliances, so that the utilization rate and fairness of network resources are effectively improved. The research is carried out in a common server-user network scene without depending on a special network scene, and large communication overhead exists among alliances.

In a mobile edge scene, a traditional wireless base station is upgraded to an intelligent base station capable of bearing more tasks, which is beneficial to communication cooperation among users. Some researches determine the mapping from the edge device to the edge server according to a load balancing strategy under the condition of multiple wireless access points, and then select the video stream with the best user experience quality on the premise of ensuring that the playing interruption does not occur. Most of the researches concern how to utilize network resources to the maximum extent under the edge computing scene, and the characteristics of fairness among users, aggregation (such as multicast) of user video content requests and the like are not deeply researched.

Disclosure of Invention

The code rate decision algorithm under the non-edge computing scene has the problems of high communication cost and difficult implementation, the existing video code rate selection method is generally driven only by a client, the client cannot obtain network state information, so that the full utilization of network resources is difficult to realize, the client generally maximizes the self income during decision making, and the fairness of video service quality among users is also influenced. Therefore, the invention provides a video code rate decision method in a mobile edge scene based on cooperative game, video users in a Multimedia Broadcast Single Frequency Network (MBSFN) area are divided into a plurality of multicast groups according to the buffer area and the channel state of the users, the same multicast group adopts the same code rate because the user states of the same multicast group are similar, the same video content only needs to be delivered and transmitted once in the group, and only needs to be taken back from a source server once, thereby improving the content delivery efficiency and reducing the load of a return link; the multicast groups effectively ensure the fairness of multiple users through the code rate and resource allocation in the cooperative game decision group.

The technical scheme of the invention is a video code rate decision method in a mobile marginal scene based on cooperative game, which comprises the following steps:

Step 1, the mobile users report the respective channel quality and the buffer area state to the respective connected base stations, the base stations continuously report the information to the edge server, and the edge server divides the users into a plurality of multicast groups according to the information;

step 2, the multicast group is used as a whole to participate in a cooperation game among the multicast groups, and the multicast server distributes bandwidth resources according to the cooperation game solution; the multicast requires the same content to be multicast in the group, so that the code rate decision in the group is consistent, and the pareto optimal solution of resource allocation and code rate decision among groups is achieved through cooperative game, namely the utility of the group cannot be increased under the condition of not reducing the utility of other groups;

and 3, completing the distribution task of the video content in the area by the multicast server, judging whether the distribution of the video content is finished or not, finishing the distribution if all the content is distributed, and returning to the step 1 to continue to circulate the process if the distribution is not finished.

Further, the method is applicable to the mobile edge network scene as follows: an edge server and a multicast server are deployed at an exchanger, the server has computing capacity and caching capacity and is connected with a plurality of base stations in the MBSFN range of a multimedia broadcast single frequency network under the management of the exchanger, the multicast single frequency network comprises one or more multicast groups which can effectively eliminate the transmission interference of adjacent MBSFN cells, a plurality of wireless devices are connected under the base stations, the exchanger is also connected with other exchangers, the edge server can carry out cost communication with the edge server in other areas, finally, the edge server is connected with a source server for storing video resources, and the edge server receives video data through the connection with the source server.

Further, the multicast server collects the multicast of the wireless link quality and the content of the users in the area, the edge server is responsible for the calculation tasks of user grouping, wireless resource allocation and code rate decision, in addition, a cache module is arranged on the edge server, the cache and the replacement of popular content are determined according to the video popularity and the cache replacement strategy, when the existing content is inquired in the group, the cache is directly provided for the group, and in addition, the cached content can be provided for the peripheral edge servers.

Further, in step 1, the edge server divides the users into a plurality of multicast groups according to the information, and specifically includes:

step 1.1, calculate grouping information

The influence of CQI and a client buffer is considered together during grouping, for the channel quality, the channel quality and the historical state information of the channel quality are considered to represent the influence of the change of the channel quality and the channel quality state, the two factors are respectively represented by CQI and CQS, and the formula of the CQS is shown as (1), wherein a is a parameter, and the CQI is a parameter_t-1Indicating the CQI at the previous time; the client buffer status takes into account two pieces of information: the first is the buffer cache size, expressed by BT, which represents the time length of the buffer cache segment in seconds; second, the average code rate of buffer is expressed by BR, and with Mbps as unit, a user is set to buffer K segments, l _kAnd (3) caching the code rate of the kth segment, wherein K is more than or equal to 1 and less than or equal to K, and the time interval of each segment is delta T, so that the BR calculation formula is shown as (2).

Step 1.2, clustering

For the N users, the N users need to be divided into M multicast groups, M is set by an edge server, each cycle needs to be grouped again, and the information of each user is a four-dimensional vector f_i，f_i＝(CQI_i，CQS_i，BT_i，BR_i) I is a user serial number; the input to the clustering algorithm is a state information dataset [ f ] for a given N users₁，f₂，...，f_n]They are divided into M groups using a clustering algorithm.

Further, in step 2, after the grouping is completed, the edge server is used as a coordination agent of the M groups, the task of resource allocation and code rate decision is completed through the calculation of the cooperative game among the M groups, and the multicast server adopts a nash bargaining model to solve the problem of the solution of the cooperative game for the multicast content of each group.

Further, the set of users is [1, 2.. N ]]The multicast group is [1, 2., M ]](ii) a One multicast server has a size of B_tThe bandwidth size is time-variable, and the bandwidths are jointly allocated to the M groups; the step 2 specifically comprises the following steps:

step 2.1, calculating user utility, namely qoe user experience quality, and u utility for downloading k segment by ith user _i(k) Indicating that the utility consists of video clip quality, quality switching, and playback interruption; q. q.s_i(k) Bitrate when downloading kth segment for user i, and size (q)_i(k) Is the storage space size of the k-th fragment, x_iThe bandwidth allocated for the user i is,_kfor the buffer status before the kth segment download, in seconds, a₁，a₂，a₃R are all parameters greater than 0, a₁，a₂，a₃Representing the proportion of the video clip quality, the quality switching and the playing interruption in the utility of the user, wherein r is 0.6;

u_i(k)＝qoe_i(k) (3)

step 2.2, multicast group utility calculation

Multicast group effect, i.e. utility function u of the multicast group_m，x_mIndicating the bandwidth occupied by the mth group,

as a flag indicating whether user i is in group m, 0 represents connected, and 1 represents unconnectedTo connect, B_tI.e. the total bandwidth under the multicast server, taking logarithm of the utility of each user connected under the base station, the bandwidth being used as denominator, a₄A weight parameter that is a bandwidth denominator to measure bandwidth cost; utility function u_mAs shown in equation (5), equations (6) and (7) are the constraints on the group utility;

step 2.3, constructing a Nash bargaining model;

and 2.4, solving the KKT condition to obtain a resource scheduling and code rate decision method.

Further, the step 2.3 of constructing the nash bargaining model specifically comprises the following steps:

To solve the bandwidth allocation and rate decision problem, it is modeled as a Nash bargaining problem, orderFor the possible protocol set space, x_mThe bandwidth, u, allocated by the protocol for group m_m(x_m) Space-based for usersThe utility of the obtained water-soluble organic fertilizer,a spatial set of bifurcation points for the user; defining a spaceA set of all possible utilities for the user, as shown in equation (8);

the utility set defining the bifurcation point is shown in equation (9).

Nash bargained outcomeThe following 4 axioms must be satisfied to become an effective bargaining result:

1) pareto optimal;

2) symmetry;

3) invariance to equivalent utility representation;

4) independent alternatives are not relevant.

There is an independent solution that satisfies the four axioms above

And can satisfy the optimization problem shown in the formula (10) and the limitation conditions (11), (12) and (13), the meaning of the formula (11) that the bandwidth allocated to all the groups does not exceed the total bandwidth B_tA flag indicating (12) whether the device i is in the group m, 0 indicates connected, 1 indicates unconnected, and (13) indicates user utility u_mNeed to be greater than or equal to the bifurcation point utility y_mThe solution found is the nash bargaining result:

u_m≥y_m，m＝1，2，...，M。 (13)

further, in step 2.4, the resource scheduling and code rate decision method obtained by solving the KKT condition is specifically as follows:

Solving the Nash bargain solving problem by using a Lagrange multiplier method, converting the optimization problem shown in (10) into a minimized logarithm sum problem shown in a formula (14) by means of a logarithm form of a continuous product, and keeping the other limiting conditions as (11), (12) and (13); then let the Lagrange multiplier be lambda, mu_mM is more than or equal to 1 and less than or equal to M, an optimization target (14) and limiting conditions (11) and (13) are constructed into a Lagrangian function L by using a Lagrangian multiplier method, as shown in a formula (15), a KKT condition solved by the problem is (16a-g), (16a) represents that the gradient is 0 when the Lagrangian function is in an extremum, and is a necessary condition for solving the optimization problem, (16b-c) is an inequality constraint condition converted from (11) and (13), (16d-e) is a Lagrangian coefficient constraint condition, a Lagrangian coefficient is not negative, (16f) is a constraint condition shown in (12), and (16g) is a relaxation complementary constraint condition of Lagrangian, and finally the Lagrangian function and the KKT condition are solved, namely a Nash solution is obtained;

y_m-u_m≤0，m＝1，2，...，M (16c)

λ≥0 (16d)

μ_m≥0 (16e)

μ_m(y_m-u_m)＝0，m＝1，2，...，M。 (16g)

has the advantages that:

the invention provides a video code rate decision method in a mobile marginal scene based on cooperative game. The invention solves the problems that the traditional video code rate selection method is only driven by a client, the utilization of network resources is not sufficient, the resource distribution among multiple users is not fair, the actual network scene dependence is lacked, the communication cost is high, the implementation is difficult and the like; due to the adoption of the multicast technology, the code rates transmitted to the clients in each group are consistent, the same multicast group adopts the same code rate, and the same video content only needs to be retrieved once from the source server, so that the content aggregation at the network edge side is realized, especially the aggregation of the high-traffic video content with high traffic degree is realized, the repeated access to the core network is greatly reduced, the load of a return link is reduced, and the method also has certain help for relieving the traffic jam of the core network; on the other hand, the cooperative game algorithm ensures the fairness among multiple users, and for the edge network, the utilization of the edge network resources is more sufficient due to the fact that edge cooperation is enhanced.

Drawings

FIG. 1: a method flow diagram of the present invention;

FIG. 2: the invention discloses a network scene schematic diagram.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by a person skilled in the art based on the embodiments of the present invention belong to the protection scope of the present invention without creative efforts.

The main process of the invention is shown in figure 1, and a video code rate decision method in a mobile edge scene based on cooperative game comprises the following steps:

step 1, firstly, mobile users report respective channel quality and buffer area state to respective connected base stations, the base stations continuously report the information to an edge server, and the edge server divides the users into a plurality of multicast groups according to the information;

step 2, the multicast group is used as a whole to participate in a cooperation game among the multicast groups, and the multicast server distributes bandwidth resources according to the cooperation game solution; the multicast requires the same content to be multicast in the group, so that the code rate decision in the group is consistent, and the pareto optimal solution of resource allocation and code rate decision among groups is achieved through cooperative game, namely the utility of the group cannot be increased under the condition of not reducing the utility of other groups;

And 3, completing the distribution task of the video content in the area by the multicast server, judging whether the distribution of the video content is finished or not, finishing the program if the distribution of all the content is finished, and returning to the step 1 to continue to circulate the process if the distribution of all the content is not finished.

Further, the mobile edge network scenario adopted by the present invention is first introduced. The invention disposes an edge server and a multicast server at a switch, the servers have computing capacity and certain caching capacity, are connected with a plurality of base stations in the Multimedia Broadcast Single Frequency Network (MBSFN) range under the control of the servers, the multicast single frequency network comprises one or a plurality of multicast groups, can effectively eliminate the transmission interference of adjacent MBSFN cells, a plurality of wireless devices are connected under the base stations, and the switch is also connected with other switches, namely the regional edge server can carry out communication with the edge server in other regions with lower cost, such as mutual communication caching and the like. The final edge server is connected to an origin server storing video resources, and the edge server receives video data through the connection with the origin server, and the specific architecture is shown in fig. 2.

The multicast server collects multicast of wireless link quality and content of users in the area, the edge server is responsible for calculation tasks such as user grouping, wireless resource allocation, code rate decision and the like, in addition, a cache module is arranged on the edge server, cache and replacement of popular content are determined according to video popularity and a cache replacement strategy, when the existing content is inquired in the group, cache can be directly provided for the group, and in addition, cached content can be provided for peripheral edge servers.

The details of each step are described in detail below.

Step 1, multicast grouping is carried out

The multicast grouping task is calculated by the edge server, and because the video content of each client is issued by the multicast server, the multicast server stores the video cache information of the client, and the channel state of the client is known to the multicast server, and then the multicast server sends the related information to the edge server for grouping calculation. The general method of group computation is described below:

step 1.1, calculate grouping information

Since the user experience quality and the video code rate quality are positively correlated, the video code rate and the client Channel Quality (CQI) are positively correlated, and the user experience quality is also affected by video quality switching and playing interruption, the impact of the CQI and the client buffer is considered in grouping. Regarding the channel quality, considering the channel quality and the historical state information of the channel quality to represent the influence of the change of the channel quality and the channel quality state, and respectively representing the two factors by using the CQI and the CQS, the formula of which is shown in (1), wherein a is a parameter, the CQI is a parameter, and the CQS is a parameter_t-1Indicating the CQI at the previous time instant. The client buffer status takes into account two pieces of information: the first is the buffer cache size, expressed by BT, which represents the time length of the buffer cache segment in seconds; second, the average code rate of buffer is expressed by BR, and with Mbps as unit, a user is set to buffer K segments, l _kAnd (3) caching the code rate of the kth segment, wherein K is more than or equal to 1 and less than or equal to K, and the time interval of each segment is delta T, so that the BR calculation formula is shown as (2).

Step 1.2, clustering

For these N users, they need to be divided into M multicast groups, where M is a variable constant set by the edge server. Since the client may be mobile and the CQI may be time varying, the packetization needs to be done anew every cycle. The information of each user is a four-dimensional vector f_i，f_i＝(CQI_i，CQS_i，BT_i，BR_i) The input to the clustering algorithm is a given set of state information [ f ] for N users₁，f₂，...，f_n]They are divided into M groups using a clustering algorithm.

Step 2, resource allocation and code rate decision

After grouping is completed, the edge server is used as a coordination agent of the M groups, the tasks of resource allocation and code rate decision are completed through calculation of cooperative game among the M groups, and the multicast server multicasts content to each group. The problem of cooperative game solution is solved by adopting a Nash bargaining model.

The user set is [1, 2.. N ], and the multicast group set is [1, 2.. M ]. A multicast server has a bandwidth with the size of Bt, the bandwidth can be time-varying, and the bandwidth is jointly allocated to M groups.

The solution to this problem is as follows:

and 2.1, calculating user utility, namely user QoE, by the user utility, wherein formulas are shown as (3) and (4). The user utility is user qoe, and the user utility when downloading segment k is composed of video segment quality, quality switch and play interruption. q. q.s_i(k) Bitrate when downloading kth segment for user i, and size (q)_i(k) Is the storage space size of the k-th fragment, x_iThe bandwidth allocated for the user i is,_kfor the buffer status before the kth segment download, in seconds, a₁，a₂，a₃R are all parameters greater than 0, a₁，a₂，a₃Representing the effects of video clip quality, quality switching and playing interruption on usersThe specific gravity of the medium-density polyethylene glycol is generally 0.6.

u_i(k)＝qoe_i(k) (3)

Step 2.2, multicast group utility calculation

I.e. the utility function u of the multicast group_m。x_mIndicating the total bandwidth occupied by the mth group,

as a flag whether device i is within group m, 0 represents connected, 1 represents unconnected, B_tI.e. the total bandwidth under the multicast server mentioned above, and this bandwidth may be time-varying. Logarithm is taken for the effectiveness of each user connected under the base station to achieve the effect of proportional fairness, the bandwidth is used as a denominator, a₄Is a weighting parameter for the bandwidth denominator to measure the bandwidth cost. Utility function u _mAs shown in equation (5), equations (6) and (7) are the constraints on the group utility.

Step 2.3, constructing Nash bargaining model

To solve the problems of bandwidth allocation and code rate decision, the present invention models it as a Nash price problem, order

For the possible protocol set space, x_mAllocating the resulting protocol bandwidth, u, for group m_m(x_m) Space-based for users

The utility of the obtained water-soluble organic fertilizer,

a spatial set of bifurcation points (protocol points free) for the user. Defining a space

The set of all possible utilities for the user is shown in equation (8).

The utility set defining the bifurcation point is shown in equation (9).

Nash bargained outcomeThe following 4 axioms must be satisfied to become an effective bargaining result:

1) pareto optimal;

2) symmetry;

3) invariance to equivalent utility representation;

4) independent alternatives are not relevant.

There is an independent solution that satisfies the four axioms aboveAnd can satisfy the optimization problem shown in the formula (10) and the limitation conditions (11), (12) and (13), the meaning of the formula (11) that the bandwidth allocated to all the groups does not exceed the total bandwidth B_tA flag indicating whether the device i is in the group m as described above in the meaning of (12), 0 indicates connected, 1 indicates unconnected, and (13) a user utility u_mNeed to be greater than or equal to the bifurcation point utility y _mThe solution found is the nash bargaining result:

u_m≥y_m，m＝1，2，...，M (13)

step 2.4, solving the KKT condition to obtain a resource scheduling and code rate decision method: the Nash bargaining solution problem is solved by using a Lagrange multiplier method. Firstly, because the optimization target contains a form of continuous multiplication, the optimization problem shown in (10) is converted into a minimized logarithm sum problem shown in an equation (14) by means of a logarithm form of the continuous multiplication, and the rest of the limiting conditions are the same as those of (11), (12) and (13). Then let the Lagrange multiplier be lambda, mu_mAnd M is more than or equal to 1 and less than or equal to M, a Lagrange multiplier method is used for constructing an optimization target (14) and limiting conditions (11) and (13) into a Lagrange function L, as shown in a formula (15), the KKT condition solved by the problem is (16a-g), (16a) represents that the gradient is 0 when the Lagrange function is in an extreme value, the requirement is used for solving the optimization problem, (16b-c) is an inequality constraint condition converted from (11) and (13), (16d-e) is a Lagrange coefficient constraint condition, the Lagrange coefficient is not negative, (16f) is the constraint condition shown in (12), and (16g) is a relaxation complementary constraint condition of Lagrange, and the Lagrange function and the KKT condition are finally solved, namely a Nash solution is obtained.

y_m-u_m≤0，m＝1，2，...，M (16c)

λ≥0 (16d)

μ_m≥0 (16e)

μ_m(y_m-u_m)＝0，m＝1，2，...，M (16g)

Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, but various changes may be apparent to those skilled in the art, and it is intended that all inventive concepts utilizing the inventive concepts set forth herein be protected without departing from the spirit and scope of the present invention as defined and limited by the appended claims.