CN117221295A - Low-delay video transmission system based on edge calculation and network slicing - Google Patents
Low-delay video transmission system based on edge calculation and network slicing Download PDFInfo
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Abstract
The invention discloses a low-delay video transmission system based on edge calculation and network slicing, which belongs to the field of data transmission and comprises an edge calculation node, a network slicing module, a content optimization module, a distributed storage module, an adaptive coding module, a security encryption module, an experience analysis module, a load expansion module, an analysis prediction module, a feedback interaction module and a user terminal; the invention can help the system to better understand the current network environment, application requirements and user behavior, is beneficial to reducing cost, improving performance and providing better user experience, can optimize the operation and planning of the system, can allocate different bandwidths and resources according to the requirements of different applications, improves the resource utilization rate, greatly reduces transmission delay and ensures that the allocation of network resources is more intelligent.
Description
Technical Field
The invention relates to the field of data transmission, in particular to a low-delay video transmission system based on edge calculation and network slicing.
Background
With the continuous development of the internet, video transmission has become an important component of network applications. However, challenges remain in video transmission, particularly in real-time video transmission where low latency and high quality are required. These challenges include network congestion, high latency, bandwidth limitations, and unstable network connections, which can lead to reduced video quality, stuck, and poor communication. With the rapid deployment of 5G networks and the rise of edge computing technologies, the field of network transmission is undergoing a revolutionary revolution. The 5G network provides higher bandwidth and lower latency, providing better conditions for real-time video transmission. While edge computation allows computation and data storage to be pushed closer to the network edge, thereby reducing transmission delay and improving data processing efficiency. However, to achieve true low-latency video transmission, network performance, resource allocation, and application requirements need to be considered in combination. This is the effect of network slicing techniques. Network slicing allows the division of physical network resources into multiple virtual network slices, each of which can be optimally configured according to the needs of a particular application, including bandwidth, latency, security, and reliability. This highly customizable network architecture enables low latency video transmission.
The existing low-delay video transmission system has higher cost, can not provide better user experience, and is inconvenient for operators to optimize the operation and planning of the system; in addition, in the existing low-delay video transmission system, we propose that the low-delay video transmission system based on edge calculation and network slicing cannot allocate different bandwidths and resources according to the requirements of different applications, and the resource utilization rate is low.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a low-delay video transmission system based on edge calculation and network slicing.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a low-delay video transmission system based on edge calculation and network slicing comprises an edge calculation node, a network slicing module, a content optimization module, a distributed storage module, a self-adaptive coding module, a security encryption module, an experience analysis module, a load expansion module, an analysis prediction module, a feedback interaction module and a user terminal;
the edge computing node is used for processing and caching video data;
the network slicing module is used for dividing the physical network resources into a plurality of virtual network slices so as to carry out optimal configuration according to application requirements;
the content optimizing module is used for analyzing and optimizing video content;
the distributed storage module is used for storing video data on a plurality of edge computing nodes;
the self-adaptive coding module is used for selecting an optimal video coding mode according to network conditions and equipment performance;
the security encryption module is used for encrypting the transmitted data, verifying the identity of a user, and detecting and defending potential network attacks;
the experience analysis module is used for monitoring and analyzing the watching behaviors of the user so as to provide personalized suggestions and recommendations;
the load expansion module is used for distributing traffic and dynamically adding or removing resources according to the requirements;
the analysis and prediction module is used for analyzing the network performance data and the user behavior data so as to predict future network demands and user viewing habits;
the feedback interaction module is used for providing feedback and participating in interaction for the user;
the user terminal is used for an interface for a user to interact with the system.
As a further scheme of the invention, the network slice module optimal configuration comprises the following specific steps:
step one: collecting and determining bandwidth requirements, delay requirements, quality of service requirements and security requirements of each application, then measuring the bandwidth capacity of each group of network links and devices and the delays of different parts in the network according to the collected application requirements, and determining whether the network devices and protocols support related QoS functions;
step two: dividing the measured total bandwidth into a plurality of groups of network slices according to application requirements, configuring proper QoS parameters for each group of slices according to the requirements, and then configuring an access control list and firewall rules to intercept access among unauthorized slices;
step three: the network monitoring tool is used for tracking the performance and resource utilization condition of each group of network slices and network congestion condition, reporting various abnormal network slices or network problems in real time, and dynamically adjusting slice configuration according to actual use conditions and requirements.
As a further aspect of the present invention, the bandwidth requirement is used to determine the bandwidth required by each application, including minimum and maximum requirements; the delay requirement is used to know the sensitivity of each application to delay to determine the maximum acceptable delay; the quality of service requirements are used to determine whether special QoS parameters need to be configured for a particular application.
As a further scheme of the invention, the video content optimization of the content optimization module comprises the following specific steps:
step 1: identifying the resolution of video and the coding format used, compressing video data and corresponding audio data by video coding technology and audio coding, and selecting the optimal resolution threshold according to equipment and bandwidth conditions;
step 2: judging whether the resolution of each video is too high according to the resolution threshold value, if the resolution of the video data exceeds the threshold value, reducing the resolution of the video data to a proper level, and then converting the file format of each video data;
step 3: and carrying out dynamic code rate self-adaption and rapid streaming on the optimized video data to an edge computing node for caching through a streaming media container format.
As a further scheme of the invention, the video data storage of the distributed storage module comprises the following specific steps:
step I: dividing each group of video data according to a specified time interval to form a plurality of groups of data blocks, generating the identification of each group of data blocks through a hash algorithm, and collecting each group of node information;
step II: selecting proper edge computing node loads to store each group of data blocks according to the data block dividing rule and the loading condition of each edge computing node through a load balancing algorithm, and after the data block is stored, carrying out configuration copying on a specified number of data blocks to a plurality of groups of edge computing node loads according to the requirements of a system and available resources;
step III: when the data stored by the edge computing node load changes, the data update is transmitted from one node to other edge computing node loads through a data synchronization algorithm, then the operation condition of each group of edge computing node loads is automatically detected, and the data migration or repair is carried out on the fault node.
As a further scheme of the invention, the experience analysis module monitors and analyzes the specific steps as follows:
step (1): collecting various knowledge and information related to the watching behaviors of each user from literature data, the Internet and a behavior database, and classifying, de-duplicating and screening the collected watching behavior knowledge;
step (2): identifying and extracting entities in the processed viewing behavior knowledge through an NLP technology, extracting corresponding attributes of each entity from related knowledge information, and establishing a relation between the entities to form a connection of a behavior knowledge graph;
step (3): processing the entity, attribute and relation into a corresponding graph structure in a triplet mode, selecting a proper graph database to store and manage the behavior knowledge graph, continuously updating and maintaining the behavior knowledge graph, then matching each group of transmitted video data with the corresponding entity in the knowledge graph, and caching the video data with consistent matching junction into the edge computing nodes.
As a further scheme of the invention, the analysis and prediction module specifically predicts the following steps:
step I: acquiring historical viewing data of a user and network demand information, preprocessing the historical viewing data to acquire characteristic data of each group, randomly dividing the characteristic data of each group into a training set, a verification machine and a test set, setting model parameters, training a neural network model through the training set, updating the parameters of the model through back propagation, and monitoring the performance of the model by using the verification set;
step II: evaluating the performance of the neural network model after training by using a test set, calculating a loss value of the model through root mean square error, minimizing the loss value through an Adam optimizer, and if the loss value does not meet a preset threshold, training the neural network model again until the loss value meets the preset threshold;
and III, step III: transmitting recent user watching data and network demand information as input data into a neural network model, then starting the input data from an input layer of the neural network model to pass through all hidden layers of the model, respectively performing linear transformation and nonlinear activation on the input data by all hidden layers, transmitting the processed data layer by layer through weights and activation functions among all layers, and then outputting a final detection result by an output layer;
IV, step: the decoder decodes the output detection result and the matching result to obtain a relevant prediction curve, and then readjusts network requirements according to the final prediction result, and simultaneously pre-caches video data which a user may watch to a user terminal and an edge computing center.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, various knowledge and information related to the watching behaviors of each user are collected from literature data, the Internet and a behavior database, the collected watching behavior knowledge is subjected to classification, de-duplication and screening, the entities in the processed watching behavior knowledge are identified and extracted through an NLP technology, the corresponding attribute of each entity is extracted from the related knowledge information, the relation among the entities is established, the connection of the behavior knowledge graphs is formed, the entities, the attributes and the relation are processed into the corresponding graph structure in a triplet form, the proper graph database is selected to store and manage the behavior knowledge graphs, the behavior knowledge graphs are continuously updated and maintained, then each group of transmitted video data is matched with the corresponding entities in the knowledge graphs, and video data consistent in the matching junction are cached in an edge computing node, so that the system can better understand the current network environment, the application requirements and the user behaviors, the cost is reduced, the performance is improved, and better user experience is provided, and the operation and planning of the system can be optimized.
2. The invention collects and determines the bandwidth demand, delay demand, service quality demand and safety demand of each application, then measures the bandwidth capacity of each group of network links and equipment and the delay of different parts in the network according to the collected application demand, determines whether the network equipment and protocol support the related QoS function, divides the measured total bandwidth into a plurality of groups of network slices according to the application demand, configures proper QoS parameters for each group of slices according to the demand, then configures an access control list and firewall rules to intercept the access among unauthorized slices, uses a network monitoring tool to track the performance and resource utilization condition of each group of network slices and network congestion condition, reports various abnormal network slices or network problems in real time, dynamically adjusts the slice configuration according to the actual use condition and the demand, can allocate different bandwidths and resources according to the demands of different applications, improves the resource utilization rate, greatly reduces the transmission delay, and ensures the allocation of network resources to be more intelligent.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a system block diagram of a low-delay video transmission system based on edge computation and network slicing according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1, a low-delay video transmission system based on edge computing and network slicing includes an edge computing node, a network slicing module, a content optimizing module, a distributed storage module, an adaptive coding module, a security encryption module, an experience analysis module, a load expansion module, an analysis prediction module, a feedback interaction module and a user terminal.
The edge computing node is used for processing and caching video data; the network slicing module is used for dividing the physical network resources into a plurality of virtual network slices so as to carry out optimal configuration according to application requirements.
Specifically, the bandwidth requirement, delay requirement, service quality requirement and security requirement of each application are collected and determined, then the delay of different parts in the network and the bandwidth capacity of each group of network links and equipment are measured according to the collected application requirement, whether the network equipment and protocol support related QoS functions or not is determined, the measured total bandwidth is divided into a plurality of groups of network slices according to the application requirement, proper QoS parameters are configured for each group of slices according to the requirement, then access interception is performed between unauthorized slices by configuring an access control list and firewall rules, the performance and resource utilization condition of each group of network slices and network congestion condition are tracked by using a network monitoring tool, various abnormal network slices or network problems are reported in real time, and meanwhile the slice configuration is dynamically adjusted according to the actual use condition and the requirement.
In this embodiment, the bandwidth requirements are used to determine the bandwidth required by each application, including minimum and maximum requirements; the delay requirement is used to know the sensitivity of each application to delay to determine the maximum acceptable delay; the quality of service requirements are used to determine whether special QoS parameters need to be configured for a particular application.
The content optimizing module is used for analyzing and optimizing the video content.
Specifically, the resolution of the video and the coding format used are identified, then video data and corresponding audio data are compressed through a video coding technology and audio coding, then an optimal resolution threshold is selected according to equipment and bandwidth conditions, whether the resolution of each video is too high or not is judged according to the resolution threshold, if the resolution of the video data exceeds the threshold, the resolution of the video data is reduced to a proper level, then the file formats of each video data are converted, and the optimized video data are subjected to dynamic code rate self-adaption and rapid streaming through a streaming media container format and are transmitted to an edge computing node for caching.
The distributed storage module is for storing video data on a plurality of edge computing nodes.
Specifically, each group of video data is divided according to a specified time interval to form a plurality of groups of data blocks, then the identifications of each group of data blocks are generated through a hash algorithm, each group of node information is collected, each group of data blocks is stored by selecting a proper edge computing node load through a load balancing algorithm according to a data block dividing rule and each edge computing node load condition, after the data block storage is completed, a specified number of data blocks are configured and copied to a plurality of groups of edge computing node loads according to the requirements and available resources of a system, when the data stored by the edge computing node loads change, data update is transmitted to other edge computing node loads from one node through a data synchronization algorithm, then the operation condition of each group of edge computing node loads is automatically detected, and data migration or repair is carried out on a fault node.
The self-adaptive coding module is used for selecting an optimal video coding mode according to network conditions and equipment performance; the security encryption module is used for encrypting the transmitted data, verifying the identity of a user, and detecting and defending potential network attacks; the experience analysis module is used for monitoring and analyzing the watching behaviors of the user so as to provide personalized suggestions and recommendations.
Specifically, various knowledge and information related to the watching behaviors of each user are collected from literature data, the Internet and a behavior database, the collected watching behavior knowledge is subjected to classification, de-duplication and screening, the entities in the processed watching behavior knowledge are identified and extracted through an NLP technology, the corresponding attribute of each entity is extracted from the related knowledge information, the relation among the entities is established, the connection of behavior knowledge graphs is formed, the entities, the attributes and the relation are processed into corresponding graph structures in a triplet mode, a proper graph database is selected to store and manage the behavior knowledge graphs, the behavior knowledge graphs are updated and maintained continuously, then each group of transmitted video data is matched with the corresponding entities in the knowledge graphs, and video data with consistent matching junction is cached in an edge computing node.
Example 2
Referring to fig. 1, a low-delay video transmission system based on edge computing and network slicing includes an edge computing node, a network slicing module, a content optimizing module, a distributed storage module, an adaptive coding module, a security encryption module, an experience analysis module, a load expansion module, an analysis prediction module, a feedback interaction module and a user terminal.
The load expansion module is used for distributing traffic and dynamically adding or removing resources according to the requirements.
The analysis and prediction module is used for analyzing the network performance data and the user behavior data so as to predict future network demands and user viewing habits.
Specifically, obtain user historical viewing data and network demand information, and obtain each group of characteristic data after preprocessing it, afterwards divide each group of data into training set, verifying machine and test set randomly, and set model parameters, train neural network model through training set, and update model parameters through back propagation, simultaneously use the performance of verification set monitoring model, evaluate neural network model performance after training is accomplished, simultaneously calculate the loss value of this model through root mean square error, and minimize the loss value through Adam optimizer, if the loss value does not meet preset threshold value, train this neural network model again until the loss value meets preset threshold value, input data from neural network model input layer through each hidden layer of model, each hidden layer carries out linear transformation and nonlinear activation on input data respectively, and carry out layer by layer transfer on the weight and the activation function between each layer on the data after handling, afterwards output layer outputs final detection result, decoder carries out the detection result to output and obtains the prediction result in order to the prediction result according to the prediction center of prediction center can be adjusted to the video, and the prediction result can be obtained simultaneously.
The feedback interaction module is used for providing feedback and participating in interaction for the user; the user terminal is used for an interface for the user to interact with the system.
Claims (6)
1. The low-delay video transmission system based on edge calculation and network slicing is characterized by comprising an edge calculation node, a network slicing module, a content optimization module, a distributed storage module, an adaptive coding module, a security encryption module, an experience analysis module, a load expansion module, an analysis prediction module, a feedback interaction module and a user terminal;
the edge computing node is used for processing and caching video data;
the network slicing module is used for dividing the physical network resources into a plurality of virtual network slices so as to carry out optimal configuration according to application requirements;
the content optimizing module is used for analyzing and optimizing video content;
the distributed storage module is used for storing video data on a plurality of edge computing nodes;
the self-adaptive coding module is used for selecting an optimal video coding mode according to network conditions and equipment performance;
the security encryption module is used for encrypting the transmitted data, verifying the identity of a user, and detecting and defending potential network attacks;
the experience analysis module is used for monitoring and analyzing the watching behaviors of the user so as to provide personalized suggestions and recommendations;
the load expansion module is used for distributing traffic and dynamically adding or removing resources according to the requirements;
the analysis and prediction module is used for analyzing the network performance data and the user behavior data so as to predict future network demands and user viewing habits;
the feedback interaction module is used for providing feedback and participating in interaction for the user;
the user terminal is used for an interface for a user to interact with the system.
2. The low-delay video transmission system based on edge computation and network slicing according to claim 1, wherein the network slicing module is optimally configured as follows:
step one: collecting and determining bandwidth requirements, delay requirements, quality of service requirements and security requirements of each application, then measuring the bandwidth capacity of each group of network links and devices and the delays of different parts in the network according to the collected application requirements, and determining whether the network devices and protocols support related QoS functions;
step two: dividing the measured total bandwidth into a plurality of groups of network slices according to application requirements, configuring proper QoS parameters for each group of slices according to the requirements, and then configuring an access control list and firewall rules to intercept access among unauthorized slices;
step three: the network monitoring tool is used for tracking the performance and resource utilization condition of each group of network slices and network congestion condition, reporting various abnormal network slices or network problems in real time, and dynamically adjusting slice configuration according to actual use conditions and requirements.
3. The low-delay video transmission system based on edge computation and network slicing according to claim 2, wherein the content optimization module video content optimization comprises the following specific steps:
step 1: identifying the resolution of video and the coding format used, compressing video data and corresponding audio data by video coding technology and audio coding, and selecting the optimal resolution threshold according to equipment and bandwidth conditions;
step 2: judging whether the resolution of each video is too high according to the resolution threshold value, if the resolution of the video data exceeds the threshold value, reducing the resolution of the video data to a proper level, and then converting the file format of each video data;
step 3: and carrying out dynamic code rate self-adaption and rapid streaming on the optimized video data to an edge computing node for caching through a streaming media container format.
4. The low-latency video transmission system based on edge computation and network slicing according to claim 1, wherein the distributed storage module video data storage comprises the specific steps of:
step I: dividing each group of video data according to a specified time interval to form a plurality of groups of data blocks, generating the identification of each group of data blocks through a hash algorithm, and collecting each group of node information;
step II: selecting proper edge computing node loads to store each group of data blocks according to the data block dividing rule and the loading condition of each edge computing node through a load balancing algorithm, and after the data block is stored, carrying out configuration copying on a specified number of data blocks to a plurality of groups of edge computing node loads according to the requirements of a system and available resources;
step III: when the data stored by the edge computing node load changes, the data update is transmitted from one node to other edge computing node loads through a data synchronization algorithm, then the operation condition of each group of edge computing node loads is automatically detected, and the data migration or repair is carried out on the fault node.
5. A low-latency video transmission system according to claim 3, wherein the experience analysis module monitors and analyzes the following specific steps:
step (1): collecting various knowledge and information related to the watching behaviors of each user from literature data, the Internet and a behavior database, and classifying, de-duplicating and screening the collected watching behavior knowledge;
step (2): identifying and extracting entities in the processed viewing behavior knowledge through an NLP technology, extracting corresponding attributes of each entity from related knowledge information, and establishing a relation between the entities to form a connection of a behavior knowledge graph;
step (3): processing the entity, attribute and relation into a corresponding graph structure in a triplet mode, selecting a proper graph database to store and manage the behavior knowledge graph, continuously updating and maintaining the behavior knowledge graph, then matching each group of transmitted video data with the corresponding entity in the knowledge graph, and caching the video data with consistent matching junction into the edge computing nodes.
6. The low-latency video transmission system based on edge computation and network slicing according to claim 1, wherein the analysis prediction module specifically predicts the following steps:
step I: acquiring historical viewing data of a user and network demand information, preprocessing the historical viewing data to acquire characteristic data of each group, randomly dividing the characteristic data of each group into a training set, a verification machine and a test set, setting model parameters, training a neural network model through the training set, updating the parameters of the model through back propagation, and monitoring the performance of the model by using the verification set;
step II: evaluating the performance of the neural network model after training by using a test set, calculating a loss value of the model through root mean square error, minimizing the loss value through an Adam optimizer, and if the loss value does not meet a preset threshold, training the neural network model again until the loss value meets the preset threshold;
and III, step III: transmitting recent user watching data and network demand information as input data into a neural network model, then starting the input data from an input layer of the neural network model to pass through all hidden layers of the model, respectively performing linear transformation and nonlinear activation on the input data by all hidden layers, transmitting the processed data layer by layer through weights and activation functions among all layers, and then outputting a final detection result by an output layer;
IV, step: the decoder decodes the output detection result and the matching result to obtain a relevant prediction curve, and then readjusts network requirements according to the final prediction result, and simultaneously pre-caches video data which a user may watch to a user terminal and an edge computing center.
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| CN117715088B (en) * | 2024-02-05 | 2024-04-26 | 苏州元脑智能科技有限公司 | Network slice management method, device, equipment and medium based on edge calculation |
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