Video routing optimization method, device and electronic device based on dynamic programming

Abstract

The present invention belongs to the field of communication, and discloses a video routing optimization method, device and electronic device based on dynamic programming. The method includes constructing a network topology diagram according to the network structure, in which vertices represent network nodes and edges represent unit transmission paths between network nodes; the network nodes include source nodes, intermediate nodes and destination nodes; constraining the input flow and output flow of the network nodes to obtain constraint conditions; constructing a state transfer equation according to the flow loss of the unit transmission path between each network node and its adjacent network nodes; and solving the state transfer equation to obtain the optimal video routing by minimizing the flow loss from the source node to each destination node as the optimization goal when the constraint conditions are met, and effectively utilizing network resources and reducing the overall transmission cost through global optimization decision-making. Moreover, the network topology diagram can be adjusted according to the changes in the network structure, so that the present invention can adapt to network structures of different scales and types.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a video routing optimization method, device and electronic equipment based on dynamic programming. Background Art

[0002] In today's digital age, video streaming has become an important part of data communications. With the increase in online video consumption, how to effectively transmit videos in the network has become a major challenge. In the process of video streaming, it is necessary not only to ensure sufficient bandwidth to meet the needs of high-definition video, but also to minimize latency and packet loss to ensure the continuity and quality of video playback. Different users have different requirements for videos, such as high definition, low latency, or high reliability. Therefore, it is particularly important to correctly and appropriately transcode the video stream.

[0003] Traditional video routing strategies usually rely on static path selection, but this strategy may not be the best choice in a dynamically changing network environment. Under this strategy, network congestion, node failure or bandwidth fluctuation will affect the quality of video streaming.

[0004] At present, video routing optimization solutions for dynamically changing network environments are often divided into the following categories. One is an optimization solution that uses simple load balancing technology based on cost or delay. This solution monitors the network status in real time and makes dynamic adjustments based on data traffic. However, this solution can usually only make dynamic adjustments based on local network information and lacks global optimization capabilities, making it difficult to find the best path under complex network conditions. Another optimization solution is to use technologies such as Multi-Protocol Label Switching (MPLS) to establish a virtual private network. Although MPLS technology improves the reliability of data transmission, its configuration is complex and difficult to expand, especially in the face of large-scale and dynamically changing network environments, the configuration and maintenance costs are high. Another optimization solution is to introduce automated and intelligent optimization solutions, such as traffic prediction and management solutions based on machine learning. This solution predicts network load and optimizes transmission routes by analyzing historical data. However, this solution requires a lot of data processing and analysis, and there may be delays in real-time applications.

[0005] Therefore, how to make the video routing optimization method dynamically adapt to changes in network status and maintain the best video transmission performance under different network conditions while making full use of network resources is currently a technical challenge. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the present invention provides a video routing optimization method, device and electronic device based on dynamic programming.

[0007] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0008] In a first aspect, the present invention provides a video routing optimization method based on dynamic programming, comprising:

[0009] Construct a network topology diagram according to the network structure; the vertices in the network topology diagram represent network nodes; the edges in the network topology diagram represent unit transmission paths between network nodes; the network nodes in the network structure include source nodes, intermediate nodes and destination nodes;

[0010] Constraining the input flow and output flow of the network nodes in the network structure to obtain constraint conditions;

[0011] A state transfer equation is constructed according to the flow loss of a unit transmission path between each network node and its adjacent network nodes;

[0012] Under the condition that the constraint conditions are met, the state transfer equation is solved with minimizing the flow loss from the source node to each destination node as the optimization goal to obtain the optimal video route.

[0013] Optionally, the constraints include:

[0014] For each network node, the maximum value of its input flow is greater than or equal to the maximum value of its output flow;

[0015] For each destination node, the maximum value of its input flow is greater than or equal to its clarity required flow value;

[0016] For the source node, the maximum value of its output flow is greater than or equal to the maximum value of the clarity required flow values ​​of all destination nodes that it can reach.

[0017] Optionally, the state transfer equation is:

[0018] ;

[0019] in, Represents a network node; yes The set of reachable destination nodes; express The adjacent network nodes; Indicates the destination node; express The clarity of the required flow value; represents a collection of unit transmission paths; Indicates from arrive The unit transmission path; express arrive Traffic loss; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; ; Represents the set of all destination nodes; for The optimal solution of ; include and ; yes A subset of yes exist The complement of ; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

[0020] Optionally, the video routing optimization method further includes:

[0021] When there are multiple source nodes, a new source node is added as a root source node, and a unit transmission path is added between the root source node and each source node; wherein the flow loss of the unit transmission path between the root source node and each source node is 0;

[0022] Constructing an extended state transfer equation according to the unit transmission path between the root source node and each source node;

[0023] Under the condition that the constraint condition is satisfied, the state transfer equation is solved with minimizing the flow loss from the source node to each destination node as the optimization goal to obtain the optimal video route, including:

[0024] Under the condition that the constraint conditions are met, the state transfer equation and the extended state transfer equation are jointly solved with the optimization goal of minimizing the flow loss from the root source node to each destination node to obtain a routing solution result;

[0025] The unit transmission path between the root source node and each source node is deleted from the routing solution result to obtain the optimal video route.

[0026] Optionally, the extended state transfer equation is:

[0027] ;

[0028] in, represents the root node; express The set of reachable destination nodes; represents the source node; represents a set of unit transmission paths between the root source node and the source node; Represents the set of all destination nodes; Indicates from arrive The unit transmission path; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

[0029] In a second aspect, the present invention provides a video routing optimization device based on dynamic programming, comprising:

[0030] A network topology diagram construction module is used to construct a network topology diagram according to a network structure; the vertices in the network topology diagram represent network nodes; the edges in the network topology diagram represent unit transmission paths between network nodes; the network nodes in the network structure include source nodes, intermediate nodes and destination nodes;

[0031] A constraint condition determination module, used to constrain the input flow and output flow of the network nodes in the network structure to obtain constraint conditions;

[0032] A state transfer equation building module, used for building a state transfer equation according to the flow loss of a unit transmission path between each network node and its adjacent network nodes;

[0033] The state transfer equation solving module is used to solve the state transfer equation to obtain the optimal video route by minimizing the flow loss from the source node to each destination node under the condition that the constraint conditions are met.

[0034] Optionally, the constraints include:

[0035] For each network node, the maximum value of its input flow is greater than or equal to the maximum value of its output flow;

[0036] For each destination node, the maximum value of its input flow is greater than or equal to its clarity required flow value;

[0037] For the source node, the maximum value of its output flow is greater than or equal to the maximum value of the clarity required flow values ​​of all destination nodes that it can reach.

[0038] Optionally, the state transfer equation is:

[0039] ;

[0040] in, Represents a network node; yes The set of reachable destination nodes; express The adjacent network nodes; Indicates the destination node; express The clarity requires flow value; represents a collection of unit transmission paths; Indicates from arrive The unit transmission path; express arrive Traffic loss; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; ; Represents the set of all destination nodes; for The optimal solution of ; include and ; yes A subset of yes exist The complement of ; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

[0041] Optionally, the video routing optimization device further includes an extended state transfer equation building module;

[0042] The extended state transfer equation construction module is used to add a new source node as a root source node when there are multiple source nodes, and add a unit transmission path between the root source node and each source node; wherein the flow loss of the unit transmission path between the root source node and each source node is 0; and construct an extended state transfer equation according to the unit transmission path between the root source node and each source node;

[0043] The state transfer equation solving module is specifically used to jointly solve the state transfer equation and the extended state transfer equation to obtain a routing solution result by taking minimizing the flow loss from the root source node to each destination node as the optimization goal while satisfying the constraint conditions; and delete the unit transmission path between the root source node and each source node from the routing solution to obtain the optimal video route.

[0044] Optionally, the extended state transfer equation is:

[0045] ;

[0046] in, represents the root node; express The set of reachable destination nodes; represents the source node; represents a set of unit transmission paths between the root source node and the source node; Represents the set of all destination nodes; Indicates from arrive The unit transmission path; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

[0047] In a third aspect, the present invention provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0048] Memory, used to store computer programs;

[0049] The processor is used to implement the method steps described in any of the above-mentioned video routing optimization methods based on dynamic programming when executing the computer program stored in the memory.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any of the above-mentioned video routing optimization methods based on dynamic programming are implemented.

[0051] The present invention provides a video routing optimization method based on dynamic programming, which constructs a network topology map according to the network structure, and then constructs and solves subsequent constraints and state transfer equations based on the network topology map. Because the network topology map can be adjusted according to changes in the network structure, the video routing optimization method provided by the embodiment of the present invention can adapt to network structures of different scales and types, and can flexibly solve different network structures to obtain the optimal video routing under the network structure.

[0052] Under the condition of satisfying the constraints, the optimization goal is to minimize the flow loss from the source node to each destination node, and the state transfer equation is solved. It can dynamically adapt to the state changes of the network. Through global optimization decisions, network resources are effectively utilized, resource waste is avoided, and the overall transmission cost is reduced.

[0053] The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of a video routing optimization method based on dynamic programming provided by an embodiment of the present invention;

[0055] Figure 2 This is a comparison chart of the traffic loss effect of the video routing optimization method based on dynamic programming and the traditional method at different network node degrees;

[0056] Figure 3 This is a comparison chart of the traffic loss effect between the video routing optimization method based on dynamic programming and the traditional method under different numbers of network nodes;

[0057] Figure 4 This is a comparison chart of the traffic loss effect between the video routing optimization method based on dynamic programming and the traditional method at different average degrees;

[0058] Figure 5 It is a structural schematic diagram of a video routing optimization device based on dynamic programming provided by an embodiment of the present invention;

[0059] Figure 6 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0061] In order to solve the problems that the existing video routing optimization methods are difficult to dynamically adapt to changes in network status, cannot maintain the best video transmission performance under different network conditions, and have low network resource utilization, the embodiment of the present invention provides a video routing optimization method based on dynamic programming, see Figure 1 , Figure 1 1 is a flow chart of a video routing optimization method based on dynamic programming provided by an embodiment of the present invention, which specifically includes the following steps:

[0062] Step S101, constructing a network topology diagram according to the network structure; the vertices in the network topology diagram represent network nodes; the edges in the network topology diagram represent unit transmission paths between network nodes; the network nodes in the network structure include source nodes, intermediate nodes and destination nodes.

[0063] In the embodiment of the present invention, a network topology diagram is constructed according to the network structure. When constructing a network topology, the network nodes in the network structure are first determined. These network nodes constitute the vertices in the network topology graph. Then, the edges in the network topology graph are determined according to the connection relationship between the network nodes. Thus, the construction of the network topology graph is completed. The connection between the network nodes can be a physical connection, such as a connection through a cable or optical fiber, or a wireless connection, such as a connection through Wi-Fi (Wireless Fidelity).

[0064] For example, a network node may be a computer, a router, or a server.

[0065] The network topology map is used to reflect the latest status of the network structure. When the network structure changes, such as adding new network nodes or changing the connection method, the network topology map can be updated accordingly.

[0066] The edges in the network topology graph represent the unit transmission paths between network nodes. For example, for network nodes and network nodes exist ,in Represents a network node and network nodes The unit transmission path between Represents the set of all unit transmission paths in the network structure, that is, all edges in the network topology graph.

[0067] In the embodiment of the present invention, the network nodes in the network structure include source nodes, intermediate nodes and destination nodes. The source node is the source of the video data and is responsible for sending the video stream to other network nodes in the network structure. The destination node is the receiving end of the video data and is responsible for receiving and playing the video stream sent from the source node. The intermediate node is neither a source node nor a destination node, and it plays a transit role in the transmission path of the video stream.

[0068] Step S102, constraining the input flow and output flow of the network nodes in the network structure to obtain constraint conditions.

[0069] The constraints on the input and output flows of network nodes include overall constraints on all network nodes and separate constraints on source nodes and destination nodes.

[0070] In order to ensure that the video stream can be transmitted smoothly and efficiently, it is necessary to constrain the input and output traffic of each network node to ensure that the input traffic of each network node is higher than the output traffic of the network node.

[0071] In order to ensure that the destination node can play the video clearly and ensure the video playback quality, the input traffic at the destination node should meet the clarity requirement of the destination node. The destination node can be understood as a receiving end or a user end.

[0072] In order to ensure the playback quality of all destination nodes reachable by the source node, the traffic output by the source node should meet the clarity requirements of all destination nodes reachable by the source node. The source node can be understood as a sender or a server.

[0073] Step S103: construct a state transfer equation according to the flow loss of a unit transmission path between each network node and its adjacent network nodes.

[0074] There is a known flow loss per unit transmission path between each network node and its adjacent network node, wherein the flow loss may be caused by the characteristics of the physical medium, the performance limitations of the network equipment, network congestion, and network interference.

[0075] For example, for a network node and its adjacent network nodes There is a known flow loss per transmission path .

[0076] State transition is a core concept in dynamic programming. In dynamic programming, state transition refers to the process of moving from one state to another, and the previous state can often affect the next state. The function of the state transition equation is to obtain the result of the next state by extending the known previous state. In the network structure, each network node does not exist independently. The flow output of a network node affects the flow input of its adjacent network nodes, and the flow loss of each unit transmission path represents the proportion of flow transmitted on the unit transmission path. For example, if from the network node To its adjacent network nodes The traffic loss is 10%, which means that only 90% of the traffic can successfully reach the network node. Therefore, the traffic loss per unit transmission path between each network node and its adjacent network nodes is crucial for the construction of the state transfer equation. The traffic between network nodes is usually dynamic, and the state transfer equation can well capture the dynamic changes of the traffic between network nodes.

[0077] Step S104, under the condition that the constraint conditions are met, the state transfer equation is solved with the optimization goal of minimizing the flow loss from the source node to each destination node to obtain the optimal video route.

[0078] In the embodiment of the present invention, the video route refers to the path of video data from a source node to a destination node, and the optimal video route refers to a video route with the smallest traffic loss and meeting the definition requirement.

[0079] In an embodiment of the present invention, the state transfer equation constructed above can be used to describe the flow of traffic between network nodes. Under the condition of satisfying the above constraints, the state transfer equation is solved with the optimization goal of minimizing the traffic loss from the source node to each destination node, and the optimal video route and the minimum traffic loss value of the optimal video route can be obtained.

[0080] In the embodiment of the present invention, a network topology map is constructed according to the network structure, and then subsequent constraints and state transfer equations are constructed and solved based on the network topology map. Because the network topology map can be adjusted according to changes in the network structure, the video routing optimization method provided by the embodiment of the present invention can adapt to network structures of different scales and types, and can flexibly solve different network structures to obtain the optimal video routing under the network structure.

[0081] Under the condition of satisfying the constraints, the optimization goal is to minimize the flow loss from the source node to each destination node, and the state transfer equation is solved. It can dynamically adapt to the state changes of the network. Through global optimization decisions, network resources are effectively utilized, resource waste is avoided, and the overall transmission cost is reduced.

[0082] In one implementation, the above constraints may include: for each network node, the maximum value of its input traffic is greater than or equal to the maximum value of its output traffic; for each destination node, the maximum value of its input traffic is greater than or equal to its clarity required traffic value; for the source node, the maximum value of its output traffic is greater than or equal to the maximum value of the clarity required traffic values ​​of all destination nodes that it can reach.

[0083] In the embodiment of the present invention, the constraint conditions can be specifically divided into path flow balance constraints, destination node flow constraints and source node flow constraints.

[0084] The path traffic balance constraint means that in order to ensure the transmission quality of each network node, for each network node, the maximum value of its input traffic is greater than or equal to the maximum value of its output traffic, that is:

[0085] ;

[0086] in, Indicates that from the network node To network node Input flow rate; Indicates that from the network node To network node output flow. Represents a network node A collection of network nodes Any network node in . Represents an enumeration All adjacent network nodes , express The set of all adjacent network nodes; Represents an enumeration All adjacent network nodes .

[0087] The destination node flow constraint means that in order to ensure that each destination node meets the clarity requirement, the maximum value of the input flow of each destination node is greater than or equal to the clarity requirement flow value, that is:

[0088] ;

[0089] in, Indicates the clarity required flow value of the destination node; Indicates that from the network node To the destination node Input flow rate; Represents the set of destination nodes; express Any destination node in the set of destination nodes.

[0090] The source node flow constraint means that in order to ensure that each destination node meets the clarity requirement, the maximum value of the output flow of the source node is greater than or equal to the maximum value of the clarity requirement flow values ​​of all destination nodes that it can reach, that is:

[0091] ;

[0092] in, From the source node To the destination node Output flow rate; Indicates the clarity required flow value of the destination node; represents the set of source nodes; express is any source node in the set of source nodes.

[0093] In the embodiment of the present invention, the maximum value of the definition required flow values ​​of all destination nodes refers to the flow value required by the destination node with the highest definition requirement among all destination nodes. For example, the definition required flow value 1 of destination node 1 is 1K, the definition required flow value 2 of destination node 2 is 2K, and the definition required flow value 3 of destination node 3 is 4K, then the maximum value of the definition required flow value is the definition required flow value 3 of destination node 3, that is, 4K.

[0094] In a real network structure, the number of source nodes may be one or more. The following will explain the optimal video routing solution process for a single source node network structure and a multi-source node network structure.

[0095] In the presence of a source node, the subgraph composed of edges with traffic will form a tree with the source node as the root and all destination nodes as leaves. This tree structure satisfies all constraints and is optimal.

[0096] First, define the dynamic programming state: Is a network node The set of reachable destination nodes, Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes in . , . Represents the collection of network nodes in the network structure, represents the set of all destination nodes. In addition, it should be noted that if such a tree does not exist, then .

[0097] Set the initial conditions of the state transfer equation: For any destination node :

[0098] ;

[0099] That is, there is no To another destination node transmission path.

[0100] For any network node ,when hour:

[0101] ;

[0102] when , Represents an empty set, that is, when there is no destination node reachable from the network node, The traffic loss of the transmission path from the network node to the destination node is infinite, that is, there is no The transmission path to the destination node.

[0103] Construct the state transfer equation:

[0104] ;

[0105] in, Represents a network node; yes The set of reachable destination nodes; express The adjacent network nodes; Indicates the destination node; express The clarity requires flow value; represents a collection of unit transmission paths; Indicates from arrive The unit transmission path; express arrive Traffic loss; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; ; Represents the set of all destination nodes; for The optimal solution of ; include and ; yes A subset of yes exist The complement of ; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

[0106] In the embodiment of the present invention, when constructing the state transfer equation, the definition requirement flow value of the destination node is taken into account. Therefore, when solving the optimal video routing, the video can be transcoded on demand according to the definition requirements of the destination node, further reducing the overall transmission cost of the video stream and avoiding the waste of network resources.

[0107] Under the condition of satisfying the constraints, the optimization goal is to minimize the flow loss from the source node to each destination node, and solve the state transfer equation to obtain the optimal video routing. ,prove Finally, for the only video source node , and the solution is This is the optimal solution, and the optimal video routing is obtained, that is, the transmission path with the minimum traffic loss from the source node to each destination node and the minimum traffic loss value.

[0108] In one implementation, when there are multiple source nodes, a new source node is added as a root source node, and a unit transmission path is added between the root source node and each source node; wherein the flow loss of the unit transmission path between the root source node and each source node is 0. The source node is an adjacent network node of the root source node.

[0109] The extended state transfer equation is constructed based on the unit transmission path between the root node and each source node:

[0110] ;

[0111] in, Indicates the root node; express The set of reachable destination nodes; represents the source node; represents a set of unit transmission paths between the root source node and the source node; Represents the set of all destination nodes; Indicates from arrive The unit transmission path; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

[0112] Accordingly, under the condition of satisfying the constraints, the state transition equation is solved with the optimization goal of minimizing the traffic loss from the source node to each destination node to obtain the optimal video routing, including:

[0113] Under the condition of satisfying the constraints, the optimization goal is to minimize the flow loss from the root source node to each destination node, and the state transfer equation and the extended state transfer equation are jointly solved to obtain the routing solution result; the unit transmission path between the root source node and each source node is deleted from the routing solution to obtain the optimal video route. and , and get the optimal video routing. Among them, it can also be decomposed recursively The state transfer equation and the extended state transfer equation are jointly solved in a way.

[0114] Based on the video routing optimization method based on dynamic programming provided by the embodiment of the present invention, a simulation experiment is carried out. In order to compare the effect of the video routing optimization method based on dynamic programming provided by the embodiment of the present invention with the traditional method, the shortest path algorithm, genetic algorithm, bee colony algorithm and ant colony algorithm are introduced as comparison under the same simulation conditions. By changing the degree of network nodes respectively, the , the number of network nodes and user distribution divergence, i.e., the average degree of network nodes , and conduct an example analysis. Among them, the degree of the network node Refers to the number of edges between a network node and other network nodes connected to it. Average degree Refers to the average number of edges between each network node and its connected network nodes.

[0115] Simulation experiment 1: Set the simulation conditions as , the number of destination nodes , and the clarity requirements of the three destination nodes are 1K, 2K and 4K respectively. They are 3, 4, and 5 respectively. For simulation results, see Figure 2 , Figure 2This is a comparison chart of the traffic loss effect of the video routing optimization method based on dynamic programming and the traditional method under different network node degrees. Figure 2 The horizontal axis represents different degrees, and the vertical axis represents the total traffic loss of video routing. Figure 2 The "dynamic programming" here refers to the video routing optimization method based on dynamic programming provided by the embodiment of the present invention, and the "shortest path" refers to the shortest path algorithm. Through simulation, it can be seen that the simulation result of the video routing optimization method based on dynamic programming provided by the embodiment of the present invention is the theoretical optimal solution, which is superior to the traditional shortest path algorithm, genetic algorithm, ant colony algorithm and bee colony algorithm in terms of loss.

[0116] Simulation experiment 2: Set the simulation condition to the number of target nodes , , respectively is 10, 12, 14, 16, 18, 20. For simulation results, see Figure 3 , Figure 3 This is a comparison chart of the traffic loss effect of the video routing optimization method based on dynamic programming and the traditional method under different numbers of network nodes. Figure 3 In the figure, the horizontal axis represents the number of different network nodes, and the vertical axis represents the total traffic loss of video routing. As the problem size required to be solved increases, the difficulty of solving the problem increases. It can be seen from simulation experiment 2 that, in this case, the video routing optimization method based on dynamic programming provided by the embodiment of the present invention shows better performance than the traditional algorithm.

[0117] Simulation experiment 3: Set the simulation conditions to ,Change 3, 4, and 5 respectively. For simulation results, see Figure 4 , Figure 4 This is a comparison chart of the traffic loss effect of the video routing optimization method based on dynamic programming and the traditional method at different average degrees. Figure 4 In the figure, the horizontal axis represents the average degree of different network nodes. , the ordinate represents the total traffic loss of video routing. Through simulation experiment 3, it can be seen that the video routing optimization method based on dynamic programming provided by the embodiment of the present invention has different The total flow loss is the lowest.

[0118] The above simulation experiments show that network operators can improve the overall efficiency of the network and the quality of video services through the video routing optimization method based on dynamic programming provided by the embodiment of the present invention, and provide users with a smoother video viewing experience. At the same time, the video routing optimization method based on dynamic programming provided by the embodiment of the present invention has a wide range of applications and is suitable for various scenarios such as the distribution of real-time data streams and large-scale data sets.

[0119] The video routing optimization method based on dynamic programming provided by the embodiment of the present invention significantly improves the efficiency and quality of video stream transmission. First, the video routing optimization method based on dynamic programming provided by the embodiment of the present invention can adapt to changes in network status in real time, reduce transmission delays by dynamically adjusting the transmission path, and ensure the continuity and high-quality playback of video streams. Secondly, through global optimization decisions, network resources are effectively utilized, resource waste is avoided, and overall transmission costs are reduced. In addition, the video routing optimization method based on dynamic programming provided by the embodiment of the present invention shows good flexibility and scalability when facing large-scale and complex network environments, and can support multi-source video transmission and large-scale video distribution networks.

[0120] Based on the same inventive concept, the embodiment of the present invention also provides a video routing optimization device based on dynamic programming, see Figure 5 , Figure 5 : is a schematic diagram of a structure of a video routing optimization device based on dynamic programming provided by an embodiment of the present invention, comprising:

[0121] The network topology diagram construction module 501 is used to construct a network topology diagram according to the network structure; the vertices in the network topology diagram represent network nodes; the edges in the network topology diagram represent unit transmission paths between network nodes; the network nodes in the network structure include source nodes, intermediate nodes and destination nodes;

[0122] The constraint condition determination module 502 is used to constrain the input flow and output flow of the network nodes in the network structure to obtain the constraint condition;

[0123] A state transfer equation building module 503, used to build a state transfer equation according to the flow loss of a unit transmission path between each network node and its adjacent network nodes;

[0124] The state transfer equation solving module 504 is used to solve the state transfer equation to obtain the optimal video route by minimizing the flow loss from the source node to each destination node under the condition that the constraint conditions are met.

[0125] In the embodiment of the present invention, a network topology map is constructed according to the network structure, and then subsequent constraints and state transfer equations are constructed and solved based on the network topology map. Because the network topology map can be adjusted according to changes in the network structure, the video routing optimization method provided by the embodiment of the present invention can adapt to network structures of different scales and types, and can flexibly solve different network structures to obtain the optimal video routing under the network structure.

[0126] Under the condition of satisfying the constraints, the optimization goal is to minimize the flow loss from the source node to each destination node, and the state transfer equation is solved. It can dynamically adapt to the state changes of the network. Through global optimization decisions, network resources are effectively utilized, resource waste is avoided, and the overall transmission cost is reduced.

[0127] Optional constraints, including:

[0128] For each network node, the maximum value of its input flow is greater than or equal to the maximum value of its output flow;

[0129] For each destination node, the maximum value of its input flow is greater than or equal to its clarity required flow value;

[0130] For a source node, the maximum value of its output flow is greater than or equal to the maximum value of the clarity required flow values ​​of all destination nodes that it can reach.

[0131] Optionally, the state transition equation is:

[0132] ;

[0133] in, Represents a network node; yes The set of reachable destination nodes; express The adjacent network nodes; Indicates the destination node; express The clarity of the required flow value; represents a collection of unit transmission paths; Indicates from arrive The unit transmission path; express arrive Traffic loss; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; ; Represents the set of all destination nodes; for The optimal solution of ; include and ; yes A subset of yes exist The complement of ; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

[0134] Optionally, the video routing optimization device further includes an extended state transfer equation building module;

[0135] An extended state transfer equation construction module is used for, when there are multiple source nodes, adding a new source node as a root source node, and adding a unit transmission path between the root source node and each source node; wherein the flow loss of the unit transmission path between the root source node and each source node is 0; and constructing an extended state transfer equation according to the unit transmission path between the root source node and each source node;

[0136] The state transfer equation solving module is specifically used to jointly solve the state transfer equation and the extended state transfer equation to obtain a routing solution result with the optimization goal of minimizing the flow loss from the root source node to each destination node under the condition of satisfying the constraint conditions; and delete the unit transmission path between the root source node and each source node from the routing solution result to obtain the optimal video route.

[0137] Optionally, the extended state transfer equation is:

[0138] ;

[0139] in, Indicates the root node; express The set of reachable destination nodes; represents the source node; represents a set of unit transmission paths between the root source node and the source node; Represents the set of all destination nodes; Indicates from arrive The unit transmission path; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

[0140] The embodiment of the present invention further provides an electronic device, such as Figure 6 As shown, Figure 6 6 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, including a processor 601, a communication interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604.

[0141] Memory 603, used for storing computer programs;

[0142] The processor 601 is used to implement the method steps described in any of the above-mentioned video routing optimization methods based on dynamic programming when executing the program stored in the memory 603.

[0143] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not represent that there is only one bus or one type of bus.

[0144] The communication interface is used for communication between the above electronic device and other devices.

[0145] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0146] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0147] The present invention also provides a computer-readable storage medium in which a computer program is stored, and when the computer program is executed by a processor, the method steps described in any one of the above-mentioned methods for optimizing video routing based on dynamic programming are implemented.

[0148] Optionally, the computer-readable storage medium may be a non-volatile memory (NVM), such as at least one disk memory.

[0149] Optionally, the computer readable memory may also be at least one storage device located away from the aforementioned processor.

[0150] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0152] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other changes of the disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the term "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and "multiple" means two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0153] The method provided in the embodiment of the present invention can be applied to electronic devices. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc. This is not limited here, and any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0154] As for the device / electronic device / storage medium embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0155] It should be noted that all embodiments of the above-mentioned video routing optimization method based on dynamic programming are applicable to the device, electronic device and storage medium provided in the embodiments of the present invention, and can achieve the same or similar beneficial effects.

[0156] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A video routing optimization method based on dynamic programming, characterized in that: include: Construct a network topology diagram based on the network structure; The vertices in the network topology graph represent network nodes; The edges in the network topology graph represent unit transmission paths between network nodes; The network nodes in the network structure include source nodes, intermediate nodes and destination nodes; Constraining the input flow and output flow of the network nodes in the network structure to obtain constraint conditions; A state transfer equation is constructed according to the flow loss of a unit transmission path between each network node and its adjacent network nodes; Under the condition that the constraint conditions are met, the state transfer equation is solved with minimizing the flow loss from the source node to each destination node as the optimization goal to obtain the optimal video route; The state transfer equation is: ； in, Represents a network node; yes The set of reachable destination nodes; express The adjacent network nodes; Indicates the destination node; express The clarity requires flow value; represents a collection of unit transmission paths; Indicates from arrive The unit transmission path; express arrive Traffic loss; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; ; Represents the set of all destination nodes; for The optimal solution of ; include and ; yes A subset of yes exist The complement of ; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

2. The video routing optimization method according to claim 1, characterized in that: The constraints include: For each network node, the maximum value of its input flow is greater than or equal to the maximum value of its output flow; For each destination node, the maximum value of its input flow is greater than or equal to its clarity required flow value; For the source node, the maximum value of its output flow is greater than or equal to the maximum value of the clarity required flow values ​​of all destination nodes that it can reach.

3. The video routing optimization method according to claim 1, characterized in that: The video routing optimization method further includes: When there are multiple source nodes, a new source node is added as a root source node, and a unit transmission path is added between the root source node and each source node; wherein the flow loss of the unit transmission path between the root source node and each source node is 0; Constructing an extended state transfer equation according to the unit transmission path between the root source node and each source node; Under the condition that the constraint condition is satisfied, the state transfer equation is solved with minimizing the flow loss from the source node to each destination node as the optimization goal to obtain the optimal video route, including: Under the condition that the constraint conditions are met, the state transfer equation and the extended state transfer equation are jointly solved with the optimization goal of minimizing the flow loss from the root source node to each destination node to obtain a routing solution result; The unit transmission path between the root source node and each source node is deleted from the routing solution result to obtain the optimal video route.

4. The video routing optimization method according to claim 3, characterized in that: The extended state transfer equation is: ； in, represents the root node; express The set of reachable destination nodes; represents the source node; represents a set of unit transmission paths between the root source node and the source node; Represents the set of all destination nodes; Indicates from arrive The unit transmission path; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

5. A video routing optimization device based on dynamic programming, characterized in that: include: A network topology diagram construction module is used to construct a network topology diagram according to the network structure; The vertices in the network topology graph represent network nodes; The edges in the network topology graph represent unit transmission paths between network nodes; the network nodes in the network structure include source nodes, intermediate nodes and destination nodes; A constraint condition determination module, used to constrain the input flow and output flow of the network nodes in the network structure to obtain constraint conditions; A state transfer equation building module, used for building a state transfer equation according to the flow loss of a unit transmission path between each network node and its adjacent network nodes; A state transfer equation solving module, used to solve the state transfer equation to obtain an optimal video route by minimizing the flow loss from the source node to each destination node under the condition that the constraint conditions are met; The state transfer equation is: ； in, Represents a network node; yes The set of reachable destination nodes; express The adjacent network nodes; Indicates the destination node; express The clarity requires flow value; represents a collection of unit transmission paths; Indicates from arrive The unit transmission path; express arrive Traffic loss; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; ; Represents the set of all destination nodes; for The optimal solution of ; include and ; yes A subset of yes exist The complement of ; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the minimum traffic loss for all destination nodes within; Indicates from Departure to The transmission path with the smallest flow loss for all destination nodes within.

6. The video routing optimization device according to claim 5, characterized in that: The constraints include: For each network node, the maximum value of its input flow is greater than or equal to the maximum value of its output flow; For each destination node, the maximum value of its input flow is greater than or equal to its clarity required flow value; For the source node, the maximum value of its output flow is greater than or equal to the maximum value of the clarity required flow values ​​of all destination nodes that it can reach.

7. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the video routing optimization method described in any one of claims 1 to 4 when executing the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the video routing optimization method according to any one of claims 1 to 4 is implemented.

Images