WO2019221352A1

WO2019221352A1 - Traffic management method in mobile edge cloud for improving quality of mobile video and device therefor

Info

Publication number: WO2019221352A1
Application number: PCT/KR2018/015395
Authority: WO
Inventors: 김석훈; 김대영
Original assignee: 순천향대학교 산학협력단
Priority date: 2018-05-15
Filing date: 2018-12-06
Publication date: 2019-11-21
Also published as: KR102046713B1

Abstract

Disclosed are a traffic management method in a mobile edge cloud for improving the quality of mobile video and a device therefor. A traffic management apparatus in a mobile edge cloud for improving the quality of mobile video, according to an aspect of the present invention, has the effect of improving the quality of experience (QoE) of a mobile video user by effectively managing the traffic of a mobile terminal by a network through the mobile edge cloud.

Description

Traffic management method in mobile edge cloud and device for improving mobile video quality

The present invention relates to a traffic management method and apparatus for the mobile edge cloud for improving the quality of mobile video, and more particularly, to improve the quality of experience (QoE) of the user through the quality of the mobile video The present invention relates to a traffic management method and apparatus for the same in a mobile edge cloud for improving the quality of mobile video.

This application claims priority based on Korean Patent Application No. 10-2018-0055647 filed on May 15, 2018, and all the contents disclosed in the specification and drawings of the application are incorporated in this application.

With the recent development of communication technologies and communication devices such as smart phones, high-speed mobile network services are possible, and anytime, anywhere access to the Internet can use mobile services. Meanwhile, as mobile services are replaced by data services rather than voice services, mobile network traffic is increasing. In this case, data traffic among the traffic generated on the mobile network occupies more than voice traffic. At this time, video traffic accounts for the largest portion of mobile data traffic, and this ratio continues to increase. However, various mobile environments affect the status of mobile networks and limit the provision of video services using the Internet. Therefore, research on how to improve the quality of experience (QoE) of video services in a mobile environment is necessary.

(Patent Document 1) Korean Registered Patent No. 10-17377860 (announced on May 19, 2017)

The present invention has been proposed to solve the above problems, and in detail, by providing a low latency and improved bandwidth to the mobile terminal using the mobile edge cloud, the quality of experience of the user through the improvement of the quality of the mobile video It is an object of the present invention to provide a traffic management method and apparatus for the mobile edge cloud for improving the quality of mobile video that can be improved.

Other objects and advantages of the present invention can be understood by the following description, and will be more clearly understood by one embodiment of the present invention. It will also be appreciated that the objects and advantages of the present invention may be realized by the means and combinations thereof indicated in the claims.

In accordance with an aspect of the present invention for achieving the above object, a mobile terminal and a base station are connected by a base station to form a mobile edge network, the traffic management device of the mobile edge cloud for providing video traffic to the mobile terminal, the mobile A status information receiver configured to receive status information of a network and a service measured from a terminal; A video traffic manager configured to learn a condition for a mobile video service using a logistic regression algorithm and manage video traffic based on network and service status information of the mobile terminal received from the status information receiver; And a video traffic distribution unit distributing the video traffic received from the content server in advance to the mobile terminal according to the condition learned by the video traffic management unit.

The video traffic management unit classifies the level of the network state of the mobile terminal using the logistic regression algorithm, and manages the video traffic by managing a media buffer according to the classified network state of the mobile terminal. .

The level of the network state is determined according to the state of mobility and mobility of the current mobile terminal.

When the determined level of network condition is low, bandwidth aggregation is generated to ensure the required bandwidth of the video player of the mobile terminal.

The video traffic management unit adjusts the amount of video traffic in the media buffer to prepare a traffic request from the mobile terminal, and from the mobile terminal the content bit rate of the video player, the data rate of the current access network, the player buffer status and the player buffer. A system parameter including a threshold value is reported to determine a boundary of a media buffer according to the system parameter, and video traffic is maintained up to the boundary to provide a video service to a mobile terminal.

When a base station movement of a mobile terminal occurs, a handover requesting and receiving transmission of cached video traffic held by the previous mobile edge computing server from a previous mobile edge computing server and requesting the next video traffic from a cloud content server on the Internet It further includes a management unit.

The handover management unit may receive a signaling message generated by the mobile terminal performing signaling through a base station and transmit the same to a cellular core network to capture information regarding when a handover occurs.

Traffic management in the traffic management device of the mobile edge cloud connected to the mobile terminal and the base station according to another aspect of the present invention for achieving the above object to form a mobile edge network, and provides video traffic to the mobile terminal The method includes receiving state information of a network and a service measured from the mobile terminal; Learning a condition for a mobile video service using a logistic regression algorithm and managing video traffic through the received network and service state information of the mobile terminal; And distributing video traffic received from a content server in advance to the mobile terminal according to the learned condition.

According to an aspect of the present invention, by effectively managing the traffic of the mobile terminal by the network through the mobile edge cloud, there is an effect of improving the quality of experience (QoE) for mobile video users.

The mobile edge cloud provides low latency and improved bandwidth utilization for mobile devices, improving the quality of experience for mobile video users.

The effect obtained in the present invention is not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the following description. .

The following drawings attached to this specification are illustrative of the preferred embodiments of the present invention, and together with the specific details for carrying out the invention serve to further understand the technical spirit of the present invention, the present invention in such drawings It should not be construed as limited to the matters described.

1 is a diagram schematically illustrating a network system of a mobile edge cloud according to an embodiment of the present invention;

2 is a schematic functional block diagram of a mobile edge computing server according to an embodiment of the present invention;

3 is a network architecture for a mobile video service in a heterogeneous network with a mobile edge cloud according to an embodiment of the present invention;

4 is a diagram illustrating a media buffer management method of a mobile edge computing server according to one embodiment of the present invention;

5 illustrates an example of traffic management when a handover occurs in a mobile edge computing server according to an embodiment of the present invention;

6 is a diagram illustrating network operation when a current access network is Wi-Fi according to an embodiment of the present invention;

7 is a schematic flowchart of a traffic management method in a mobile edge computing server according to an embodiment of the present invention.

The above objects, features and advantages will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, whereby those skilled in the art may easily implement the technical idea of the present invention. There will be. In addition, in describing the present invention, when it is determined that the detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Throughout the specification, when a part is said to "include" a certain component, it means that it can further include other components, without excluding other components unless otherwise stated. In addition, the “…” described in the specification. “Unit” refers to a unit that processes at least one function or operation, which may be implemented in hardware or software, or a combination of hardware and software.

1 is a diagram schematically illustrating a network system of a mobile edge cloud according to an embodiment of the present invention, and FIG. 2 is a schematic functional block diagram of a mobile edge computing server according to an embodiment of the present invention.

In describing the present embodiment, the mobile edge cloud may be deployed in the mobile edge network to monitor the status of the mobile terminal 100. On the other hand, the mobile edge cloud is also referred to as mobile edge computing. Mobile edge computing moves computing resources from the Internet cloud to the access network, and the mobile edge computing server 200 configures the mobile edge cloud for the access network. Thus, mobile terminal 100 can use a mobile edge cloud for computing.

Meanwhile, in the present embodiment, the traffic management apparatus of the mobile edge cloud for improving the quality of mobile video may be a mobile edge computing server 200.

The mobile edge computing server 200 manages video traffic according to the state of the mobile edge network and the mobility of the mobile terminal 100, and provides video traffic to the mobile terminal 100. In this case, video traffic may be provided in advance from the content server 400 on the Internet.

The mobile edge computing server 200 evaluates the state of the access network and performs traffic management. In this case, the mobile edge computing server 200 receives and manages video traffic from the content server 400 in advance, and provides the video traffic to the mobile terminal 100 to maintain a seamless service of the mobile terminal 100. do. In addition, the mobile edge computing server 200 monitors the bandwidth required for the video service of the mobile terminal 100 and the mobile terminal 100 to combine the cellular network and the Wi-Fi network to compensate for the insufficient bandwidth. ) Can be instructed.

According to the present embodiment, since mobile edge computing has moved the function of the Internet cloud to the access network, data storage and analysis, which is conventionally performed in the Internet cloud, is performed in the access network. In more detail, the mobile edge computing server 200 performs storage and analysis of data. As such, since the storage and analysis of the data are performed at a short distance with the edge device (mobile terminal 100) rather than a long distance, a quality of experience (QoE) for a service may be improved.

Meanwhile, the key to mobile video traffic management is to maintain the bandwidth required for video services in heterogeneous networks. The prior art method involves switching the network (network switching) between cellular and Wi-Fi to get better bandwidth in heterogeneous networks or to manage the overall bandwidth as common radio resources (CRR). Network switching schemes assume that networks with better signal strength can provide better bandwidth, but high signal strength does not guarantee better bandwidth due to various network elements. In general, a combination of service elements is used to switch (switch) a network in a heterogeneous network. The common radio resource (CRR) satisfies the service request bandwidth by selecting an appropriate network according to the usage of all radio resources in the heterogeneous network. Typical traffic management uses user preferences to ensure the required bandwidth. According to the user preference, the mobile terminal 100 selects a network for the mobile video service and manages the buffer to ensure the required bandwidth. The video player on the mobile terminal 100 controls the amount of requested traffic for the mobile video service according to the available bandwidth. However, by managing the traffic using only the information measured by the mobile terminal 100, it is difficult to adapt to changes in the network state, so that the computational load of the mobile terminal 100 increases. Improvements in QoE for mobile video services require a method for traffic management that can reduce the load on the mobile terminal 100 and quickly adapt to the access network. Therefore, a method of managing the bandwidth and video traffic by the mobile edge computing server 200 in the mobile edge cloud for the mobile terminal 100 will be described below.

Referring to FIG. 1, a mobile edge computing server 200 according to the present embodiment is connected to a base station 300 of an access network and provides a cloud service to a mobile terminal 100 of an access network. . In other words, the mobile edge computing server 200 is connected by the mobile terminal 100 and the base station 300 to form a mobile edge network, and provides video traffic to the mobile terminal 100. In this case, the access network may be any one of a cellular network and / or a Wi-Fi network. Meanwhile, according to the present embodiment, the mobile terminal 100 that lacks resources may use storage and computing resources of the mobile edge computing server 200. The access network is connected to the mobile edge computing server 200 of the mobile edge cloud, and the mobile edge computing server 200 can monitor the traffic transmitted in the access network.

Referring to FIG. 2, the mobile edge computing server according to the present embodiment includes a state information receiver 210, a video traffic manager 220, and a video traffic distributor 230.

The state information receiver 210 receives state information of a network and a service measured from a mobile terminal.

The video traffic manager 220 learns a condition for the mobile video service through the network and service status information of the mobile terminal received from the status information receiver 210. The video traffic manager 220 learns the conditions for the mobile video service using a logistic regression algorithm and manages the video traffic.

The video traffic manager 220 may classify the network state of the mobile terminal using a logistic regression algorithm, and manage the video traffic by managing a media buffer according to the classified network state of the mobile terminal. At this time, the level of the network state may be determined according to the state of the access network and / or mobility of the current mobile terminal. On the other hand, the video traffic management unit 220 may generate a bandwidth aggregation to ensure the required bandwidth of the video player of the mobile terminal when the determined level of network conditions is low.

The video traffic manager 220 adjusts the amount of video traffic in the media buffer to prepare a traffic request from the mobile terminal, and from the mobile terminal the content bit rate of the video player, the data rate of the current access network, the player buffer status and the player buffer. A system parameter including a threshold may be reported to determine a boundary of a media buffer according to the above-described system parameter, and video traffic may be maintained up to the determined boundary to provide a video service to a mobile terminal.

The video traffic distributor 230 distributes the video traffic received from the content server in advance to the mobile terminal according to the conditions learned by the video traffic manager 220.

Meanwhile, the mobile edge computing server according to the present embodiment further includes a handover manager 240.

When the base station movement of the mobile terminal occurs, the handover management unit 240 requests and transmits the cached video traffic held by the previous mobile edge computing server from the previous mobile edge computing server, and receives the next request from the cloud content server on the Internet. Request video traffic. Meanwhile, the handover manager 240 may receive a signaling message generated by the mobile terminal performing signaling through the base station and transmit the signaling message to the cellular core network, thereby capturing information regarding when the handover occurs.

Meanwhile, the traffic management in the mobile edge cloud of the mobile edge computing server according to the present embodiment will be described in detail with reference to FIGS. 3 to 6.

3 is a network architecture for a mobile video service in a heterogeneous network with a mobile edge cloud according to an embodiment of the present invention, and FIG. 4 is a diagram illustrating a media buffer management method of a mobile edge computing server according to an embodiment of the present invention. 5 is a diagram illustrating an example of traffic management when a handover occurs in a mobile edge computing server according to an embodiment of the present invention, and FIG. 6 is a Wi-Fi network of a current access network according to an embodiment of the present invention. Is a diagram illustrating network operation when.

Referring to FIG. 3, the mobile terminal 100 is connected to a heterogeneous network (HN) composed of a cellular network and a Wi-Fi network, and the video player of the mobile terminal 100 reproduces the traffic received while the user moves. In the cellular network, the mobile terminal 100 may use a mobile edge cloud because it is operated by a carrier. Operators can open the mobile edge cloud to partner service providers. The mobile edge computing server 200 is connected to the base station 300 of the cellular network to form a mobile edge cloud. In a network architecture, Wi-Fi networks are coupled to cellular networks. Accordingly, the mobile edge cloud obtains network state information of the mobile terminal through data traffic delivered from the network. That is, the mobile terminal 100 measures their network and service status information and reports this information to the mobile edge computing server 200. Thereafter, the mobile edge computing server 200 may obtain overall state information about the mobile terminal 100. The mobile edge computing server 200 learns the best condition for the mobile video service through the state information of the mobile terminal 100. The mobile edge computing server 200 receives video traffic from the content server 400 in advance and distributes the video traffic to the mobile terminal 100 according to the network condition. In order to distribute the video traffic received to the mobile terminal 100, the amount of traffic received at the mobile edge computing server 200 depends on the learning results of the mobile edge computing server 200. The mobile edge computing server 200 uses a logistic regression algorithm for learning and classifies the network state using the network information of the mobile terminal 100 through the algorithm. When the mobile edge computing server 200 performs learning for data analysis and the mobile terminal 100 reports the measured information, the computational load of the mobile terminal 100 is not large, and the mobile edge computing server 200 is The overall network state and service state of the mobile terminal 100 may be considered. Therefore, stable video traffic distribution can be achieved in the mobile edge cloud, thereby maintaining the quality of service of the mobile video service.

Meanwhile, mobile video services are affected by various network environments such as latency, congestion, and packet loss. In order to satisfy the QoE for the mobile video user, the mobile terminal 100 must keep the rate (rate) of the received video traffic higher than the content bit rate (rate) of the video player. At this time, the rate of the received video traffic depends on the network conditions. Thus, there is a need for adaptive traffic management in the mobile edge cloud as network conditions change. The mobile edge cloud knows the state of the access network, allowing you to effectively manage traffic based on the state of the network. Factors affecting the operation of the video player of the mobile terminal 100 may be network state change and mobility. If the access network is in poor condition, video traffic will not be reliably received. As a result, the video player may be buffered and the video service may be interrupted. Buttering of the video player and interruption of the video service can reduce the QoE for the service of the mobile video user. In addition, the mobility of the mobile terminal 100 may also cause a change in the network state.

According to this embodiment, bandwidth aggregation can be used to ensure the required bandwidth of mobile video. In heterogeneous networks, bandwidth aggregation can be used to satisfy the quality of service (QoS) of video services. It is important to accurately predict the current network state for the efficiency of bandwidth aggregation, which may be done by the mobile edge computing server 200. Mobile edge computing server 200 also creates a media buffer for each mobile terminal 100 and manages the amount of video traffic in the buffer. The mobile edge computing server 200 provides video traffic to the mobile terminal 100 on behalf of the content server 400 in the Internet cloud via a media buffer.

Meanwhile, the network status level depends on the current access network and the mobility. The mobile edge computing server 200 learns through the learning whether the access network is in good or bad condition. In addition, the mobile edge computing server 200 predicts whether the mobile terminal 100 moves or stops (mobility) through learning. Thereafter, the mobile edge computing server 200 determines a network state level as shown in Table 1 below.

If the network status level is low, it is difficult to guarantee the content bit rate required by the video player of the mobile terminal 100. This means that bandwidth aggregation is likely to occur to ensure the required bandwidth of the video player. According to the network status level, the mobile edge computing server 200 manages a media buffer for the video service of the mobile terminal 100. By adjusting the amount of video traffic in the media buffer, the mobile edge computing server 200 prepares the traffic request from the mobile terminal 100. The mobile terminal 100 includes system parameters such as content bit rate (π _t ) of the video player, data rate (rate) (λ _t ) of the current access network, player butter state (B _t ) and player buffer threshold (THD). Report a variable (parameter). The mobile edge computing server 200 determines the boundary of the media buffer B _m according to the system parameter (parameter). The mobile edge computing server 200 maintains video traffic to the perimeter to provide video services to the mobile terminal 100. The boundary of the media buffer can be divided into three stages: low boundary (LB), high boundary (HB), and MAX. If the access network environment is poor, the mobile terminal 100 tries to satisfy the content bit rate of the video player, and a low latency is required. Because of the low latency of receiving video traffic, the mobile edge cloud retains more video traffic. Thereafter, the mobile terminal 100 may obtain video traffic from the media buffer of the mobile edge cloud instead of the content server 400. Meanwhile, Tables 2 and 4 illustrate media buffer management of the mobile edge computing server 200.

On the other hand, handover may occur when the mobile terminal 100 moves to another base station 300 area. The mobile terminal 100 determines the handover process according to the signal strength of the base station 300 or the radio channel quality. Since the mobile edge computing server 200 is integrated into the base station 300 of the cellular network, handover may affect the use of the mobile edge cloud. According to this embodiment, the old mobile edge computing server 200 and the new mobile edge computing server 200 exchange information with each other through signals when a handover occurs. Thus, when the mobile terminal 100 moves to another base station 300 during the mobile video service, the new mobile edge computing server 200 retains on the old mobile edge computing server 200 from the old mobile edge computing server 200. Requires the transmission of cached video traffic. The new mobile edge computing server 200 then requests the next video traffic from the internet cloud content server 400.

As shown in FIG. 5, the mobile edge computing server may perform traffic management in the mobile edge cloud when handover occurs. Since mobile video requires an HTTP range request in bytes, the mobile edge computing server 200 manages video traffic in bytes. If a handover occurs, the new mobile edge computing server 200-2 may request the remaining video traffic from the old mobile edge computing server 200-1. Until the new mobile edge computing server 200-2 receives the remaining video traffic, the mobile terminal 100 maintains the mobile video service through the traffic filled in the player buffer. After the remaining traffic is transferred between the old mobile edge computing server 200-1 and the new mobile edge computing server 200-2, the new mobile edge computing server 200-2 sends the next range of video traffic to the content server. request.

On the other hand, when a handover occurs, the mobile terminal 100 performs signaling for seamless service. The signaling message is delivered to the cellular network element via base station 300. In this case, the mobile edge computing server 200 is connected to the base station 300. The signaling message is sent to the cellular core network via the mobile edge computing server 200. Thus, the mobile edge computing server 200 can capture a signaling message that includes information about when a handover occurs. When the mobile edge computing server 200 captures this signaling message to obtain handover information, the mobile edge computing server 200 signals the amount of remaining traffic of the cached video traffic of the mobile edge computing server 200. During the procedure, it can send to the new mobile edge computing server 200-2.

On the other hand, it is important to ensure sufficient bandwidth for video player requirements to ensure QoE. For stable traffic management, the network must be controlled, and the mobile edge computing server 200 determines an access network condition through system parameters (parameters) reported by the mobile terminal 100. According to the network state, the mobile edge computing server 200 may recommend the network operation to the mobile terminal 100. When there is a large change in access network state and mobility, the mobile edge computing server 200 may attempt to control the access network to ensure the bandwidth required for the video player of the mobile terminal 100 through bandwidth aggregation. Tables 3 and 6 below show network operation when the current access network is Wi-Fi.

To improve the QoE of mobile video users, bandwidth aggregation is used when bandwidth for video playback is urgently needed. In addition, if the mobile terminal is not mobile, it is good to connect to another WiFi network. When the current access network is cellular, network operation is simpler. The mobile terminal attempts to connect to Wi-Fi according to the mobile phone's policy. When the mobile terminal is connected to the Wi-Fi, it may perform the operations described in Table 3 above. If the mobile terminal has low mobility in cellular, such as the current access network, bandwidth aggregation can be performed by connecting to a nearby Wi-Fi to compensate for the lack of bandwidth for video playback. According to this embodiment, the network status level is important. The network status level can be determined by the network status value and the mobility value. Network state values and mobility values are obtained by learning from the mobile edge computing server. The learning algorithm may be used for estimation of a state of a wireless network or decision making in a network, and the above-described values (network state value and mobility value) may be calculated by a classification algorithm of machine learning. The mobile edge computing server according to the present embodiment processes the entire information of the access network and has sufficient resources for computing, so that it can learn about network conditions.

Classification by regression analysis may be applied to determine network status and mobility status. That is, learning about network conditions may be performed by logistic regression algorithm. This classification method is used to classify binomial states. Since the network state has a good state and a bad state, and the mobility state has a mobile state and a stationary state, appropriate regression analysis can be considered in determining the network state and the mobile state. State classification requires an input feature for learning. In the case of network conditions, the throughput (or transmit data rate) and received signal strength indication (RSSI) values are used as input functions x1, x2. In the mobility state, the difference in RSSI value per unit time and the difference in the number of Wi-Fi APs per unit time are used as the input functions x1 and x2. Then, the learning algorithm for state classification is as follows. When the state (state for network or mobility) is represented by y, it has a binomial distribution as in Equation 1 below.

In the network state, if y is 1, it is good, otherwise (y is 0) it is bad. In the state of mobility, y is 1, otherwise it is stationary (y is 0). Logistic regression uses a logistic function to construct a hypothesis and uses a sigmoid function with a value between 0 and 1 as a logistic function. It is represented by Equation 2 below.

Equation 2 has an input parameter composed of a linear function by weight θ and input feature x. The state probability of the weight θ is expressed using the log function.

Then, Equations 3 and 4 may be expressed as Equation 5 below.

The mobile edge computing server collects training cases during the mobile video service. There are m training examples, which are generated independently on the mobile edge computing server. Therefore, Equation 5 may be expressed as a likelihood function for m training examples as shown in Equation 6 below.

Equation 6 may be rewritten as a log likelihood function. Equation 6 is expressed as a polynomial through a log likelihood function.

Given Equation 2, Equation 7 becomes a function of the weight θ. Through the weight θ, the mobile edge computing server makes a decision about the state. Therefore, an appropriate weight θ must be determined to increase the accuracy of the decision about the state. The appropriate weight θ can be obtained by maximizing the log likelihood function in equation (7). Thus, the mobile edge computing server updates the weight θ using a gradient ascent algorithm.

In Equation 8, α is a unit size of a gradient of a log likelihood function. The slope of the log likelihood function for the j th input feature is expressed by Equation 9 below.

If Equation 9 is applied to Equation 8, Equation 8 becomes Equation 10 below.

Training examples include the hypothesis h (x) and outcome (y). Learning the i th learning example updates the weights for the j th input feature. The weight is determined using a stochastic gradient ascent rule. In the stochastic gradient ascent rule, the update to the weights is repeated until the weights converge. This is expressed as

Mobile edge computing servers have plenty of computing resources for a lot of data and learning. This learning allows the mobile edge computing server to correctly determine the network state and mobility state. After accurate state awareness, the proposed traffic management can improve QoE for mobile video users.

Referring to FIG. 7, first, a mobile edge computing server receives network and service state information measured from a mobile terminal (S710).

Thereafter, the mobile edge computing server learns the conditions for the mobile video service using the logistic regression algorithm through the state information of the network and the service of the received mobile terminal and manages the video traffic (S720).

Next, the mobile edge computing server distributes the video traffic received from the content server in advance to the mobile terminal according to the learned condition (S730).

Methods according to an embodiment of the present invention may be implemented in the form of program instructions that may be implemented as an application or executed through various computer components, and may be recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the computer-readable recording medium may be those specially designed and constructed for the present invention, and may be known and available to those skilled in the computer software arts. Examples of computer readable recording media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs, DVDs, and magneto-optical media such as floptical disks. media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform the process according to the invention, and vice versa.

While this specification includes many features, such features should not be construed as limiting the scope of the invention or the claims. In addition, the features described in the individual embodiments herein can be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment of the present specification can be implemented individually in various embodiments or in combination as appropriate.

Although the operations have been described in a particular order in the drawings, they should not be understood as being performed in a particular order as shown or in a sequence of successive orders, or all of the described actions being performed to obtain a desired result. Multitasking and parallel processing may be advantageous in certain circumstances. In addition, it should be understood that the division of various system components in the above-described embodiments does not require such division in all embodiments. The app components and systems described above may generally be packaged in a single software product or multiple software products.

The present invention described above is capable of various substitutions, modifications, and changes without departing from the technical spirit of the present invention for those skilled in the art to which the present invention pertains. It is not limited by the drawing.

Claims

In the traffic management device of the mobile edge cloud is connected by a mobile terminal and a base station to form a mobile edge network, and provides video traffic to the mobile terminal,

A status information receiver configured to receive status information of a network and a service measured from the mobile terminal;

A video traffic manager configured to learn a condition for a mobile video service using a logistic regression algorithm and manage video traffic based on network and service status information of the mobile terminal received from the status information receiver; And

And a video traffic distribution unit for distributing the video traffic received from the content server in advance to the mobile terminal according to the condition learned by the video traffic management unit.
The method of claim 1,

The video traffic management unit,

Traffic management of the mobile edge cloud by using the logistic regression algorithm to classify the level of the network state of the mobile terminal, and manage the video traffic by managing the media buffer according to the level of the network state of the classified mobile terminal Device.
The method of claim 2,

The level of the network state is a traffic management device of the mobile edge cloud is determined according to the current state of the access network and mobility of the mobile terminal.
The method of claim 3, wherein

If the level of the determined network condition is low,

Device for traffic management of the mobile edge cloud, characterized in that bandwidth aggregation occurs to ensure the required bandwidth of the video player of the mobile terminal.
The method of claim 3, wherein

The video traffic management unit,

A system that adjusts the amount of video traffic in a media buffer to prepare a traffic request from a mobile terminal, and includes from the mobile terminal the content bit rate of the video player, the data rate of the current access network, player buffer status, and player buffer threshold. The media management device according to the system parameter to determine the boundary of the media buffer, the traffic management device of the mobile edge cloud, characterized in that to maintain the video traffic to the boundary to provide a video service to the mobile terminal.
The method of claim 1,

When a base station movement of a mobile terminal occurs, a handover requesting and receiving cached video traffic retained by the previous mobile edge computing server from a previous mobile edge computing server and requesting the next video traffic from a cloud content server on the Internet Traffic management device of the mobile edge cloud further comprising a management unit.
The method of claim 6,

The handover management unit,

The mobile terminal receives a signaling message generated by performing the signaling through the base station, and transmits it to the cellular core network to capture information about when the handover occurs.
A traffic management method in a traffic management apparatus of a mobile edge cloud connected by a mobile terminal and a base station to form a mobile edge network and providing video traffic to the mobile terminal,

Receiving status information of a network and a service measured from the mobile terminal;

Learning a condition for a mobile video service using a logistic regression algorithm and managing video traffic through state information of the network and service of the received mobile terminal; And

And distributing video traffic received from a content server in advance to the mobile terminal according to the learned condition.