1 Introduction

With the widespread promotion of Internet multimedia applications and the rapid development of [12] and wireless communication technology [14], the demand for real-time multimedia services by mobile terminals such as mobile phones is increasing. Guo [4] is increasing. However, the multimedia service is facing a series of problems in response to large-scale parallel service delay [15], unlimited resources uneven distribution of [7, 11] in low quality of user experience, so the academic and communication industry researchers carried out a series of research work, and achieved a series of achievements.

On the one hand, a new approach based on game theory to change rate, modulation and power in the game algorithm was proposed in article [1]. The authors of article [6] proposed a resource management system with transmission capacity for enhancing the performance of wireless mesh networks. The work of article [10] studied an enhanced radio resource management based MIH policies in heterogeneous wireless networks. Song B et al. [13] proposed a two-stage approach for task and resource management in multimedia cloud environment.

On the other hand, a key tool of common radio resource management technique had been designed by the author of article [9] to jointly manage the radio resources from different radio access technologies. The contributions of Castro A et al. [3] the proposal of an extended service management architecture based on Multi agent Systems able to integrate the fault diagnosis with other different service management functionalities. The resource scheduling for content dissemination in multimedia cloud was proposed by authors of article [8], which could increase the user satisfaction and decrease completion time of content dissemination.

The authors of article [16] investigated secure multimedia big data application in trust-assisted sensor cloud and proposed two types: one with a single trust value threshold), and another with multiple trust value thresholds. A qualitative analysis of electronic Human Resource Management was conducted by the author of article [2]. Y. Jin et al. proposed a QoS guaranteeing mechanism with the error control aware and the importance of ARQ Block with IEEE 802.16e protocol [5]. However, this research work ignored the problem of resource management in multimedia communication.

However, no in-depth study on the mobile resources for large-scale user equilibrium, communication network resource allocation and server large-scale computing problems, so this thesis studies the wireless resource management mechanism for independent multimedia streaming green communication.

2 Model of multimedia green communication system

The multimedia green communication system is based on the standard multimedia communication system, which is used to monitor and control the energy for reducing resource consumption and improving the utilization of resources. The multimedia server, the relay base station and the user cluster are combined, and the integrated energy utilization rate of multimedia communication is the main line, and its architecture is shown in Fig. 1. The multimedia green communication system mainly consists of three parts: the multimedia server cluster, the green communication module and the communication sub-net.

Fig. 1
figure 1

Multimedia green communication system architecture

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Here, the multimedia server cluster include the distributed parallel multimedia streaming servers. They deploy in different regions and connect with different green communication modules. The full duplex channel must be used in the communication sub network and the green communication module, which include the energy utilization rate collection and feedback special channel. In addition, there are a large heterogeneous relay base station and a cluster of diverse user clusters in the communication sub-net. The user terminal of the user cluster is dominated by various types of smart phones. The energy report collection connects the green computing module and the communication sub-net, Its role is to report the energy utilization to the multimedia server cluster.

$$ \left\{\begin{array}{l}\overline{U_n}=\sum \limits_{i=1}^{\left|n-m\right|}{C}_i{U}_{n-i}\\ {}\overline{V_n}=\sum \limits_{i=1}^{\left|n-m\right|}{C}_{n-i}{\overline{U}}_n\end{array}\right. $$
(1)

Among them, \( \overline{U_n} \) denotes the mean of the application requirements of the N user is expressed. \( \overline{V_n} \) denotes the resource status of the N relay base station. M represents the number of peer-to-peer data streams. Cn represents the weight of the first n green communication.

After the \( \overline{U_n} \) and \( \overline{U_n} \) are obtained by formula (1), the N relay base station is selected with \( \sum \limits_{l=1}^n{\overline{U}}_n\overline{V_n}>1 \), otherwise the next iteration will be carried out.

The relay cluster terminal is connected with the user cluster control point through the multimedia coding interface. Its role is to receive the multimedia data service request and the user terminal energy usage evaluation. The link can also be used for the identification and reorganization of the wireless channel and multimedia transmission channel, the related system information and paging information needed for cluster interaction control.

The green communication module is made up of a dedicated computer cluster, and the architecture and protocol stack are shown in Fig. 2. Among them, the competition module connects the service access point with the protocol stack. Specially, this module would activate the new round of service access point competition process through the peer interaction and control signaling of the protocol stack. At the same time, we classify the computers of the green communication computer cluster with the service access points dynamically, also considering the multi stream parallel business scale and the protocol stack state. In order to load balanced cluster connection, the service access point not only has the basic function of communication with special computer, but also support cluster service related control signaling, cluster scheduling and paging message sending, cluster service communication network and multimedia server cluster. There are the mappings between multimedia wireless link parallel flow establishment, monitoring and evaluation etc. Besides, cluster green communication control point is not static assignment, but periodic or irregular internal competition allocation according to the size of multimedia flow and the size of user request. This is to avoid the problem of network performance degradation caused by poor state terminals, while giving full play to the advantages of excellent state terminals, avoiding resource waste and supporting the function of fault weakening.

Fig. 2
figure 2

Green communication module

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The mapping function in Fig. 2 is shown as formula (2), in which the function represents a balanced evaluation method between the user’s requirements and the communication sub-net in a two-dimensional space.

$$ \left\{\begin{array}{l}\varphi \left(u,v\right)=\sum \limits_{i=1;j=1}^{n;m}\varphi \left({u}_{n-i},{v}_{m-j}\right)\\ {}\varphi \left(x,y\right)=\sum \limits_{k=1}^{\left|n-m\right|}\frac{C_{n-k}{C}_{m-k}}{nm}\sqrt{\iint \varphi \left(x,y\right) dxdy}\end{array}\right. $$
(2)

3 Autonomous management mechanism of multimedia wireless resources

In order to satisfy the diversity of user applications and dynamic service requirements, the different wireless access technologies and resource allocation schemes would be employed in multimedia communication. Based the above state, the radio resource management of real-time, accuracy and balance would be disturbed seriously, resulting in poor quality of user experience and network load distribution problem of polarization. In order to effectively improve the efficiency of resource management in multimedia service of large-scale heterogeneous wireless networks, we have refined the above problems into the following key issues.

  1. (1)

    how to construct dynamic multimedia wireless resource architecture in dynamic service demand and asynchronous multimedia communication environment.

  2. (2)

    how to ensure the balance of the distribution of the wireless multimedia service resources and speed up the convergence rate of the resource allocation;

  3. (3)

    how to improve the autonomy of multimedia wireless resource management.

On the basis of the multimedia green communication system established in second section, with the perspective of green communication module, we built the multimedia wireless resource dynamic architecture shown in Fig. 3 by considering the diversity, dynamism and heterogeneity. Among them, dynamic architecture is embodied in dynamic monitoring and dynamic service control and statistical modules. The module periodically exchanges information with the green communication module. The user diversity requirement is refined into a N subset. These subsets are used as the basis for network load balancing. There are two links between the wireless access control and the autonomous control port set. One links is used to transfer the user feedback of the wireless access state. The other is used to completing their interaction. In particular, the diversity of users’ needs and the interaction between modules and dynamic service control and statistical modules is the core control flow in Fig. 3. The format is shown in Table 1. The parameter description of the signaling field defined in Table 1 is detailed in Table 2.

Fig. 3
figure 3

Multimedia wireless resource dynamic architecture

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Table 1 Interaction signaling composition
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Table 2 Important parameters for interactive signaling
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The autonomous management mechanism of multimedia wireless resources is based on the optimization goal defined by formula (3), and the algorithm pseudo code of Fig. 4 is used to manage resources independently.

$$ \left\{\begin{array}{l}\min \sum \limits_{i=1}^N\varphi \left({u}_i,{d}_i\right)\\ {}s.t.\sum \limits_{i=1;j=1}^{n;m}{f}_{ij}\left(\overline{\varphi_i}\right)\\ {}{u}_i\in U\kern0.5em {d}_i\in D\kern0.5em \overline{\varphi_i}=\frac{\sum \limits_{i=1}^N\varphi \left({u}_i,{d}_i\right)}{N\left|n-m\right|}\end{array}\right. $$
(3)
Fig. 4
figure 4

An autonomous management algorithm for multimedia wireless resources

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Among them, U represents a subset of user diversity requirements, and D represents a set of dynamic service statistics, \( \overline{\varphi_i} \) representing the independent management statistics and asynchronous means of multimedia wireless resources.

4 Performance evaluation and result analysis

In order to verify performance of the proposed radio resource management mechanism for multimedia stream green communications (WRMG), such as the performance of the server and relay network source management performance and user experience, we designed a set of experiments, which verified the above performance in different scale parallel server multimedia stream under the delay, number of users under different scale relay the proportion of idle resources, network user satisfaction and packet loss rate performance. The above performance is compared with ones of the multimedia transmission protocol proposed in reference [5] as WMAP-16.

The definition of experimental index:

  1. (1)

    the delay of the server in the parallel multimedia stream of different sizes: with the increase of parallel multimedia stream, the maximum delay of the server side to the user’s demand.

  2. (2)

    the proportion of idle resources in relay networks under different scales of users: as the number of users increases, the total surplus idle resources of relay networks occupy the proportion of total resources.

  3. (3)

    packet loss rate: in the end to end communication process from the server side to the user’s mobile terminal, the loss of data packets is in the proportion of the total transmission packets.

  4. (4)

    user satisfaction: the user’s actual satisfaction on the video at the mobile terminal is divided into 5 levels, detailed in Table 4.

Tables 3 and 4 gives the setting of the experimental environment and parameters.

Table 3 Experimental environment
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Table 4 User satisfaction rating
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On the one hand, we statistically analyzed the real-time performance of the proposed algorithm and the efficiency of network resource management in the face of different scale parallel multimedia traffic and user scale, as shown in Figs. 5 and 6. Figure 5 shows the delay of the server in parallel multimedia streams of different sizes. With the increase of parallel multimedia traffic, the resource consumption of servers on multimedia request listener channel is increasing, increasing the response delay to new multimedia cases. This delay will continue to spread with the creation of multimedia stream, the reading and reconfiguration of multimedia data, so that the real time experience of the user is seriously affected. However, from Fig. 5, we can see that the proposed WRMG algorithm is divided into multimedia server cluster, green communication module and communication sub-net through the multimedia green communication system. At the same time, the multimedia server cluster is composed of distributed parallel multimedia streaming servers deployed in different regions and connected by green communication modules. Especially, compared with WMAP-16, the energy report collection of the proposed algorithm connects the green computing module and the communication sub-net, and reports the energy utilization to the multimedia server cluster. The above control processes can equalize the overload of multimedia server in the communication sub-net. These processes can also enable the server to maintain real-time performance in the case of large-scale parallel multimedia streaming. Similarly, the proposed algorithm can efficiently cope with the radio resource allocation and balancing problem of relay networks under different scale users. According to the energy report, we collect resource information and integrate resources in real time, which effectively improves the resource utilization rate. See Fig. 6.

Fig. 5
figure 5

Latency of servers in parallel multimedia streams of different sizes

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Fig. 6
figure 6

The proportion of idle resources of relay network under different scale of users

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The experience and performance of the user side are detailed in Figs. 7 and 8. The proposed algorithm is based on the green communication module and reconstructs the multimedia wireless resource dynamic architecture from diversity, dynamics and heterogeneity. Dynamic monitoring and dynamic service control and statistical experiment multimedia architecture are updated dynamically. In particular, compared with WMAP-16, the proposed algorithm refines the user diversity requirement for network load balancing. Among them, wireless access control and autonomous control port set can not only feed back the wireless access state, but also realize the independent interaction. In particular, the diversity of user needs and the interaction between modules and dynamic service control and statistical module can effectively reduce packet loss rate and improve user satisfaction.

Fig. 7
figure 7

Packet loss rate

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Fig. 8
figure 8

Mean Opinion Score

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5 Conclusions

For resolving the delay and multimedia server overload problems in massive parallel multimedia streaming and user access, a wireless multimedia communication independent green resources management mechanism and its architecture were proposed in order to short service response delay and improve the utilization rate of wireless resources, which could ensure the quality of the multimedia mobile user experience. On the one hand, the multimedia real-time service system is divided into multimedia server, relay base station and user cluster. The multimedia green communication system architecture and its green communication control algorithm are designed to optimize the comprehensive energy utilization of multimedia communication. On the other hand, in order to adapt to the dynamic service requirements and asynchronous multimedia communication environment, a dynamic multimedia wireless resource architecture is proposed. The architecture can effectively balance the wireless multimedia service resources and quickly converge the resource allocation algorithm. We use the methods of mathematical analysis and experimental statistics to compare the proposed algorithms in terms of real time, reliability and effectiveness, respectively. The results show that compared with the QoS guarantee mechanism of document [5], the proposed algorithm has obvious advantages in terms of server response delay, relay network idle resource ratio, user satisfaction and packet loss rate.