Video stream-oriented special wireless protocol stack and service orchestration method

Abstract

The invention discloses a video stream-oriented special wireless protocol stack and a service orchestration method, and the special wireless protocol stack is constructed into a cloud-side-end system architecture, and comprises a core network, a servitization RAN (Radio Access Network) and a terminal device: the core network is deployed at a cloud video application server for connection; the servitization RAN is deployed at the edge of a network to provide wireless access service for the terminal equipment, and the servitization RAN comprises a service management network element and a local AI service; the terminal equipment is wirelessly connected with the service RAN and uploads video stream data acquired by a camera of the terminal equipment through the service RAN by adopting different processing strategies according to different resolutions; for a 4K video stream, when the service RAN finds a local AI service, the 4K video stream acquired by the terminal equipment is shunted to the local AI service deployed at the edge of a network to be processed; and for the 480P video stream, the servitization RAN directly transmits the uplink of the 480P video stream to a video application server deployed at a cloud end through a core network for intelligent analysis and processing.

Description

Technical Field

[0001] The present invention relates to a video stream data transmission processing technology, and in particular to a dedicated wireless protocol stack and service arrangement method for video streams. Background Art

[0002] With the rapid development of wireless communication technology, the bandwidth and transmission capacity of mobile networks have been significantly improved, and the demand for the transmission of high-definition video streams in mobile networks has become more and more common. Especially in the fields of video surveillance, intelligent security, virtual reality, etc., the demand for real-time transmission and intelligent analysis of high-definition video data is increasing. Traditional wireless network architecture mainly focuses on the transmission of general business traffic, while there is relatively little research on the efficient transmission and intelligent processing of high-definition video streams, especially video streams with a resolution of 4K or above, in wireless networks.

[0003] In the current technological context, cloud computing and edge computing have become important means of intelligent analysis of video streams. Cloud computing is suitable for processing large-scale and complex video data analysis tasks, but due to the large amount of high-definition video data, processing in the cloud will occupy a lot of network bandwidth and may cause high latency. In order to reduce the network burden and service latency, edge computing came into being, migrating the intelligent analysis tasks of some video streams to the edge nodes of the network for processing. However, the existing wireless protocol stack is difficult to adapt to such complex application scenarios. Therefore, the design of a dedicated wireless protocol stack for high-definition video streams has become a hot topic in current research. Summary of the invention

[0004] The purpose of the present invention is to provide a dedicated wireless protocol stack and service orchestration method for video streaming, so as to solve the following problems existing in the prior art:

[0005] 1) Network bandwidth pressure: 4K video streams have a huge amount of data, and the traditional RAN (Radio Access Network) transmits all video data to the cloud for processing. This method not only occupies a large amount of backhaul link bandwidth, but also increases data transmission latency, affecting real-time performance.

[0006] 2) Computing resource optimization problem: In practical applications, video analysis requirements vary in different scenarios. For example, intelligent analysis of 480P video streams can be directly processed by cloud video servers; however, due to the large amount of data, 4K video streams need to be initially analyzed on the edge side to reduce the amount of data transmitted back. However, existing technologies lack an effective mechanism to dynamically discover and utilize edge computing resources to achieve optimal processing of video streams with different resolutions.

[0007] 3) Protocol stack adaptability issues: The existing RAN protocol stack is difficult to flexibly adapt to different video streaming scenarios and lacks dedicated support for large data volume scenarios such as 4K video streaming, resulting in low system processing efficiency.

[0008] To achieve the above object, the present invention provides a dedicated wireless protocol stack for video streaming, which is constructed as a cloud-edge-end system architecture, including a core network, a service-oriented RAN and a terminal device, wherein:

[0009] The core network is deployed in the cloud, connected to a video application server also deployed in the cloud, and is responsible for processing and storing video stream data from the terminal;

[0010] The service-oriented RAN is deployed at the edge of the network to provide wireless access services for the terminal device, wherein the service-oriented RAN includes a service management network element and a local AI service also deployed at the edge of the network, the local AI service runs on an AI server, and the service-oriented RAN dynamically discovers and utilizes the local AI service through the service management network element to process the intelligent analysis task of the 4K video stream;

[0011] The terminal device is wirelessly connected to the service-oriented RAN, and uploads the video stream data collected by its camera through the service-oriented RAN using different processing strategies according to different resolutions, specifically:

[0012] For 4K video streams: when the service-oriented RAN discovers the local AI service, the 4K video stream collected by the terminal device is diverted to the local AI service deployed at the edge of the network for processing;

[0013] For 480P video stream: the service-oriented RAN directly transmits the 480P video stream uplink through the core network to the video application server deployed in the cloud for intelligent analysis and processing.

[0014] In one embodiment of the present invention, the service-oriented RAN adopts the service-oriented and modular design principles to decompose the traditional radio access network functions into independent service components, and further includes:

[0015] Service-oriented RAN control plane: including CUCP: responsible for radio resource management, session management and mobility management control functions;

[0016] Service-oriented RAN user plane: including CUUP: responsible for processing the transmission of user data, including data encapsulation, encryption and compression;

[0017] Data flow distribution service: responsible for distributing user plane data to different processing paths;

[0018] Service orchestrator: responsible for dynamically orchestrating and optimizing service instances based on business needs and network policies;

[0019] Decision-making and control function: Make decisions on starting, stopping, scaling up or down service instances based on network policies and business needs;

[0020] Digital object management function: responsible for managing the status and configuration of digital objects in the network;

[0021] Service scheduling function: Responsible for the specific scheduling and scaling operations of service instances based on the decisions made by the decision control function;

[0022] Network Function Service Interface: responsible for defining the communication protocols and interfaces between services, and supporting the interoperability and modularity of services;

[0023] Tunnel conversion service: responsible for establishing and maintaining data transmission tunnels between the user plane and edge computing nodes;

[0024] Video stream forwarding module: responsible for sending video stream data packets to the local AI server via UDP to achieve local diversion and processing of video streams;

[0025] Service-oriented RAN prototype verification system: used to test and verify the functions of the service-oriented RAN architecture and components, supporting end-to-end business processes, including a dedicated 4K video prototype service-oriented RAN system and a dedicated non-4K video prototype service-oriented RAN system.

[0026] In one embodiment of the present invention, the service-oriented RAN prototype verification system defines a radio bearer mechanism for large video stream intelligent analysis, namely, VF DRB. The implementation of the radio bearer mechanism specifically includes:

[0027] DRB configuration and data forwarding settings: When the user's terminal device accesses the network, the CUUP of the service-oriented RAN checks whether the DRB to which the uploaded data packets belong is a VF DRB. If so, the data packets are forwarded to the AI ​​server for processing before reaching the NGU of the service-oriented RAN;

[0028] Setting of video stream forwarding module: The video stream forwarding module is integrated in the CUUP uplink transmission processing component. Its main functions include: sending the 4K video stream data packet submitted by the CUUP UL thread to the AI ​​server through UDP; configuring the VF DRB identifier through the Telnet protocol; querying NRF to obtain the network address of the AI ​​server;

[0029] A data adaptation module is also added to the AI ​​server to process the video stream data transmitted from the video stream forwarding module. The data adaptation module is an independent process that runs on the AI ​​server together with the video server. The main functions of the data adaptation module include: creating a virtual network card on the AI ​​server so that the video analysis application can monitor and process the data received through the virtual network card; adapting the video stream data sent by the video stream forwarding module into data that can be parsed by the AI ​​server and submitting it to the virtual network card; and

[0030] Define the interaction rules between the video stream forwarding module and CUUP UL thread (task, video stream forwarding module and NRF, and video stream forwarding module and data adaptation module.

[0031] In one embodiment of the present invention, the interaction rules between the video stream forwarding module and the CUUP UL thread task are specifically as follows:

[0032] The video stream forwarding module configures the ID of the VF DRB to the CUUP UL thread task. The CUUP UL thread task decides whether to submit the processed data packet to the task of the video stream forwarding module or the UPF of the core network based on the configuration.

[0033] In one embodiment of the present invention, the interaction rules between the video stream forwarding module and the NRF are specifically as follows:

[0034] The video stream forwarding module queries NRF for the IP address of the AI ​​server and the port number of the data adaptation application through HTTP requests.

[0035] In one embodiment of the present invention, the interaction rules between the video stream forwarding module and the data adaptation module are specifically as follows:

[0036] The data adaptation module receives data packets of the UDP tunnel from the physical network card, i.e., data packets after the service-oriented RAN diverts the video stream data packets uploaded by the terminal device through the tunnel conversion service;

[0037] The data adaptation module obtains the original video stream data packet;

[0038] The data adapter module writes the data into the virtual network card;

[0039] The video server reads the video stream data from the virtual network card.

[0040] In one embodiment of the present invention, the specific process of the service-oriented RAN discovering the local AI service includes:

[0041] Step S101: The tunnel switching service in the service-oriented RAN registers the service with the RNRF module in the service-oriented RAN through the HTTP interface. Specifically, the tunnel switching service sends a service registration request to the RNRF module, and the RNRF module returns a service registration response to the tunnel switching service. The RNRF module is a network service discovery and subscription module for supporting service discovery and subscription.

[0042] Step S102: After the local AI service is started, the local AI service registers the service with the RNRF module in the service-oriented RAN through the HTTP interface;

[0043] Step S103: The service-oriented RAN obtains the service name and IP address / port information of the local AI service, and replies with a service registration response;

[0044] Step S104: the service-oriented RAN queries the health status of the local AI service according to a preset timing;

[0045] Step S105: When the device terminal initiates a query for a local AI service, it is sent to the core network via the service-based RAN, and the core network returns the query result to the device terminal 103 via the service-based RAN.

[0046] In one embodiment of the present invention, a dedicated non-4K video prototype service-oriented RAN system is used for the offloading of 480P video stream data, and the non-4K video stream collected by the terminal device is uploaded to the cloud core network through the service-oriented RAN. The specific process is as follows:

[0047] Step S201: the service-oriented RAN queries the local AI service and adopts the service-oriented RAN protocol stack for non-4K video streams;

[0048] Step S202: After the terminal device accesses the wireless network, the terminal device queries the network for local AI services;

[0049] Step S203: The camera of the terminal device collects video stream data;

[0050] Step S204: the terminal device does not find the local AI service, and the terminal device sends the collected non-4K video stream data to the service-based RAN;

[0051] Step S205: the service-oriented RAN receives the uplink video stream data service from the terminal device, and pushes the video stream data to the video application server through the core network.

[0052] In one embodiment of the present invention, a dedicated 4K video prototype service-oriented RAN system is used for offloading 4K video stream data, and the specific process is as follows:

[0053] Step S301: The service-oriented RAN discovers the local AI service, queries the metadata of the local AI service, including the service name and IP address / port information, and switches to the service-oriented RAN protocol stack dedicated to 4K video streaming;

[0054] Step S302: After the terminal device accesses the wireless network, the terminal device queries the network for local AI services;

[0055] Step S303: The camera of the terminal device collects video stream data;

[0056] Step S304: After the terminal device queries the local AI service, the terminal device sends the collected video stream data to the service-based RAN;

[0057] Step S305: the service-oriented RAN receives the uplink video stream data service from the terminal device, and after identifying the local diversion identifier, performs local IP network diversion on the uplink video stream data of the terminal device, specifically, pushes the video stream data to the tunnel conversion service through the UDP transmission protocol;

[0058] Step S306: The tunnel conversion service performs tunnel relationship adaptation on the diverted video stream data, and pushes the video stream data to the target local AI service;

[0059] Step S307: The local AI service performs corresponding intelligent analysis on the uplink video stream data.

[0060] The present invention also provides a video stream-oriented service arrangement method, which is implemented by the aforementioned dedicated wireless protocol stack and specifically includes:

[0061] S1. The decision control function of the service-oriented RAN initiates a request to the computing plane control function to launch the AI ​​analysis service for edge video stream data.

[0062] S2. The computing surface control function feeds back the computing resource scheduling results to the decision control function;

[0063] S3. The decision control function initiates a request to the service scheduling function to schedule the service-oriented RAN protocol stack dedicated to 4K video streaming and the AI ​​analysis service of edge video streaming data;

[0064] S4. The service scheduling function pulls up a service-oriented RAN protocol stack function instance dedicated to 4K video streaming;

[0065] S5. The service scheduling function launches the AI ​​analysis service for edge video stream data.

[0066] Compared with the prior art, the dedicated wireless protocol stack and service arrangement method for video streaming disclosed in the present invention have at least the following advantages:

[0067] 1) Improved network resource utilization efficiency: Through the cloud-edge-end collaborative system architecture, the present invention can process and analyze 4K video stream data in real time at the edge node, thereby significantly reducing the backhaul bandwidth required for video stream transmission and reducing the burden on the core network. This reasonable allocation and optimization of resources effectively improves the utilization efficiency of the entire network;

[0068] 2) Reduced video stream processing latency: By introducing a dedicated 4K video stream protocol stack in the service-oriented RAN, the present invention can perform intelligent traffic diversion and processing on the RAN side of the data transmission path, reducing the latency of video stream data on the transmission link. In particular, through the design of the video stream forwarding module (VSS), the video stream can be quickly processed in the access network, greatly shortening the time from acquisition to analysis and improving the real-time performance of the system.

[0069] 3) Enhanced flexibility and adaptability of the system: Through the dynamic service orchestration method, the present invention can flexibly switch between 480P video stream cloud analysis and 4K video stream edge analysis to meet the needs of different application scenarios. This flexibility not only adapts to the changing network environment, but also ensures the efficient operation and service quality of the system;

[0070] 4) Efficient intelligent analysis of video streams: This invention uses edge AI services to perform local analysis and processing of 4K video streams, combined with the intelligent offload technology of service-oriented RAN, which greatly improves the processing efficiency of video stream data. By reducing the bottlenecks caused by long-distance transmission and centralized processing, the system can perform intelligent analysis of video streams more quickly, thereby improving the response speed and accuracy of applications;

[0071] 5) Improved system scalability: The dynamic discovery and interaction mechanism between the service-oriented RAN and local AI services proposed in the present invention enables the system to expand or reduce computing resources and service instances according to demand, thereby enhancing the scalability of the system. Whether in small-scale deployment or large-scale applications, the system can adapt to different loads and processing requirements through dynamic adjustment;

[0072] 6) Optimized the transmission and processing of video data: Through the collaborative work of the data adapter module (DA) and the VSS module, the present invention realizes the efficient transmission of video stream data from the physical network card to the virtual network card in the data transmission path, ensuring the efficiency and stability of the video data in the processing link, which not only improves the processing capability of the system, but also further reduces the data packet loss rate and delay in network transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0074] Figure 1 A schematic diagram of a system architecture according to an embodiment of the present invention;

[0075] Figure 2 Used to illustrate the data flow direction of local forwarding of video stream data of the present invention;

[0076] Figure 3 Schematic diagram of the interaction between the video stream forwarding module task and the CUUP UL thread task in one embodiment of the present invention;

[0077] Figure 4 A schematic diagram of the interaction between the video stream forwarding module task and NRF in one embodiment of the present invention;

[0078] Figure 5 A schematic diagram of the interaction between the video stream forwarding module task and the data adaptation module in one embodiment of the present invention;

[0079] Figure 6 A schematic diagram of a format for splitting video stream data packets uploaded by a terminal device in one embodiment of the present invention;

[0080] Figure 7 This is a schematic diagram of the format of a video stream data packet pushed by a service-oriented RAN to an AI server in one embodiment of the present invention;

[0081] Figure 8 A schematic diagram of a process of discovering a local AI service by a service-oriented RAN in an embodiment of the present invention;

[0082] Fig. 9 This is a schematic diagram of a processing flow of 480P video stream data in one embodiment of the present invention;

[0083] Fig.10 This is a schematic diagram of a 4K video stream data splitting process in one embodiment of the present invention;

[0084] Fig.11 A schematic diagram of service arrangement for video scenarios in one embodiment of the present invention;

[0085] Fig.12 A schematic diagram of RNRF module service discovery in one embodiment of the present invention;

[0086] Fig.13 A schematic diagram of the RNRF module service discovery and subscription process in one embodiment of the present invention;

[0087] Fig.14 This is a schematic diagram of the RNRF module service registration process in one embodiment of the present invention;

[0088] Fig.15 The figure is a schematic diagram of the RNRF module service subscription / query process in one embodiment of the present invention.

[0089] Explanation of reference numerals: 101 - core network; 102 - service-oriented RAN; 103 - terminal device; 104 - video application server; 105 - local AI service; 106 - tunnel switching service; 107 - RNRF module. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] Before describing the embodiments of the present invention, the relevant key technologies involved in the present invention are described as follows:

[0092] (1) Service-Oriented RAN: Service-Oriented RAN technology modularizes and services the functions of wireless access networks, and can dynamically arrange network functions according to application scenarios to implement customized network services for different business needs. The present invention uses service-oriented RAN to flexibly distribute and process video streams, especially to sink the intelligent analysis tasks of 4K video streams to edge nodes, thereby reducing the burden on the backhaul network;

[0093] (2) Edge Computing: Edge computing deploys computing and storage resources at the edge of the network close to the data source, which can process data close to the user, thereby reducing latency and bandwidth consumption. In this invention, edge computing is used to perform real-time AI intelligent analysis of 4K video streams, significantly improving the system's response speed and resource utilization efficiency;

[0094] (3) Dedicated wireless protocol stack: To meet the different processing requirements of 4K and 480P video streams, the present invention designs two dedicated wireless protocol stacks. The traditional cloud analysis mode is used for 480P video streams, while the edge analysis is performed through a dedicated wireless protocol stack for 4K video streams, thereby optimizing the overall performance of the system;

[0095] (4) Video Stream Intelligent Analysis: Video stream intelligent analysis technology involves real-time processing and analysis of high-definition video content, using AI algorithms to identify and process key events in the video. By deploying such analysis tasks on edge nodes, the present invention can significantly reduce data transmission volume and service latency;

[0096] (5) Dynamic service orchestration: The dynamic service orchestration function in the present invention enables the system to flexibly schedule and switch different protocol stacks and service instances according to real-time network and business requirements, thereby achieving optimized processing of video streams with different resolutions.

[0097] Figure 1 FIG. 1 is a schematic diagram of a system architecture of an embodiment of the present invention. Figure 1 As shown, this embodiment provides a dedicated wireless protocol stack for video streaming, which is constructed as a cloud-edge-end system architecture to support intelligent analysis of video streams in high-definition video surveillance scenarios. The system architecture includes a core network 101, a service-oriented RAN 102, and a terminal device 103, wherein:

[0098] The core network 101 is deployed in the cloud and connected to the video application server 104 (DN server in the figure) which is also deployed in the cloud, and is responsible for processing and storing the video stream data from the terminal; wherein the video server deployed in the cloud is mainly used to process the intelligent analysis request from the 480P video stream;

[0099] The service-oriented RAN 102 is deployed at the edge of the network to provide wireless access services for the terminal device 103. The service-oriented RAN 102 includes a service management network element and a local AI service 105 also deployed at the edge of the network. The local AI service 105 runs on an AI server. The service-oriented RAN 102 dynamically discovers and uses the local AI service 105 through the service management network element to process the intelligent analysis task of the 4K video stream. The local AI service 105 deployed at the edge of the network receives the 4K video stream from the terminal device 103 through UDP (User Datagram Protocol), processes and analyzes it in real time, and transmits the processing result back to the video application server 104 in the cloud through the service-oriented RAN 102.

[0100] Among them, the network element (NE) refers to a device or software entity with specific functions and independent operation capabilities in the communication network. They are the basic units that constitute the communication network and are responsible for completing various communication tasks. The network element can be a physical device, such as a switch, a router, a base station, etc., or it can be a software entity, such as a protocol stack, an application, etc. The service management network element of this embodiment is a key component (or module) in the service-oriented RAN 102, which is used to discover, call, and manage various services in the service-oriented RAN 102. The service management network element enables the system to flexibly select the most appropriate service and protocol stack according to the resolution of the video stream (such as 4K or 480P) and business requirements, so as to achieve efficient video stream processing and intelligent analysis. This dynamic service orchestration function is a key component of the service-oriented RAN 102 architecture, which improves the flexibility and adaptability of the network while optimizing the efficiency of resource utilization.

[0101] In this embodiment, the service-oriented RAN (radio access network) is a new type of network architecture, which adopts the service-oriented and modular design principles to decompose the traditional radio access network functions into independent service components to improve the flexibility, scalability and automation of the network. The following is an overview of some key functions and modules in the service-oriented RAN:

[0102] Service-oriented RAN control plane (Control-plane): including CUCP (Central Unit Control-plane): responsible for control functions such as radio resource management, session management, and mobility management;

[0103] Service management network element: responsible for service registration, discovery, subscription and lifecycle management;

[0104] Service-oriented RAN user plane (User-plane): including CUUP (Central Unit User-plane): handles the transmission of user data, including data encapsulation, encryption, compression, etc.;

[0105] Data flow distribution service: responsible for distributing user plane data to different processing paths, such as the core network or edge computing nodes;

[0106] Edge Computing: includes local AI services: performing intelligent analysis of video streams, data processing and other tasks at the edge of the network to reduce latency and bandwidth consumption.

[0107] Service orchestrator: responsible for dynamically arranging and optimizing service instances based on business needs and network policies, such as intelligent analysis tasks for video streams;

[0108] Policy and Control Function (PCF): Makes decisions on starting, stopping, scaling, and other aspects of service instances based on network policies and business needs.

[0109] Digital Object Management Function (DOMF): manages the status and configuration of digital objects (such as cells, user equipment, etc.) in the network;

[0110] Service Scheduling Function (SSF): responsible for the specific scheduling and scaling of service instances based on the decisions of PCF;

[0111] Service-based Interfaces: define the communication protocols and interfaces between services, and support the interoperability and modularity of services;

[0112] Tunnel conversion service: responsible for establishing and maintaining data transmission tunnels between the user plane and edge computing nodes;

[0113] Video Stream Steering (VSS): responsible for sending video stream data packets to the AI ​​server via UDP to achieve local distribution and processing of video streams.

[0114] Data Adaptation Module (DA): On the AI ​​server, the video stream data transmitted from the VSS module is adapted into a format that can be parsed by the server;

[0115] Service-oriented RAN prototype verification system: used to test and verify the functions of the service-oriented RAN architecture and components, supporting end-to-end business processes, including a dedicated 4K video prototype service-oriented RAN system and a dedicated non-4K video prototype service-oriented RAN system.

[0116] The terminal device 103 is wirelessly connected to the service-oriented RAN 102, and uploads the video stream data collected by its camera through the service-oriented RAN 102 using different processing strategies according to different resolutions, specifically:

[0117] For 4K video streams: When the service-oriented RAN 102 discovers the local AI service 105, the 4K video streams collected by the terminal device 103 will be diverted to the local AI service 105 deployed at the edge of the network for processing, so as to reduce the bandwidth occupancy of the backhaul link and reduce latency; wherein the local AI service 105 includes an application server dedicated to processing 4K video streams;

[0118] For 480P video stream: the service-oriented RAN 102 directly transmits the 480P video stream uplink through the core network 101 to the video application server 104 deployed in the cloud for intelligent analysis and processing.

[0119] The terminal device 103 uploads the video stream data collected by the camera through its built-in camera video acquisition application, and the service-oriented RAN 102 determines the resolution of the video stream data:

[0120] If the video stream data is a 480P video stream, the service-oriented RAN 102 schedules its service-oriented RAN protocol stack dedicated to non-4K video streams to directly transmit the 480P video stream uplink to the cloud;

[0121] If the video stream data is a 4K video stream, the service-oriented RAN 102 queries the access network system through its service management network element whether there is an available local AI service 105: if yes, the service-oriented RAN 102 schedules its service-oriented RAN protocol stack dedicated to 4K video streams, and pushes the 4K video stream uploaded by the terminal device 103 to the local AI service 105 for processing; if no, the service-oriented RAN 102 schedules its service-oriented RAN protocol stack dedicated to 4K video streams, and pushes the 4K video stream uploaded by the terminal device 103 to the cloud for processing;

[0122] The purpose of the local AI service 105 of this embodiment is to directly process the video stream locally. The bandwidth requirement for transmitting the AI ​​analysis results is much higher than that for transmitting the video directly. The difference between the processing of 480P and 4K video streams is that the 480P video stream is allowed to be pushed directly to the cloud, while the 4k video stream is processed locally first. It is preferred to look for local AI services available locally. If there are local AI services, they will be processed locally. If not, they need to be uploaded to the cloud server for processing. The 4K video stream dedicated protocol component is designed to realize intelligent diversion of large video stream data on the RAN side to ensure that the 4K video stream can be effectively transmitted and processed.

[0123] Figure 2 The data flow used to illustrate the local forwarding of the video stream data of the present invention is as follows: Figure 2 As shown, the data streams uploaded by the terminal device through the wireless network include DRB#1 (used to indicate 480P video stream data) and DRB#2 (used to indicate 4K video stream data). The DRB#1 data stream is uploaded to the cloud through the edge service-oriented RAN, and the cloud returns the processing results, and forwards the DRB#2 data stream to the edge service for processing.

[0124] In this embodiment, a wireless bearer mechanism for intelligent analysis of large video streams, namely, VF DRB (Video Forwarding Data Radio Bearer) is defined in the service-oriented RAN prototype verification system. When a user equipment (UE) accesses the network, a dedicated VF DRB is created for the 4K video stream to directly forward all 4K video stream data packets to the AI ​​server for processing. The implementation of the wireless bearer mechanism specifically includes:

[0125] DRB configuration and data forwarding settings: When a user equipment (UE) accesses the network, the CUUP (Central Unit User-plane) of the servitized RAN 102 checks whether the DRB (Data Radio Bearer) to which the data packets uploaded by the user equipment belong is a VF DRB. If so, these data packets are forwarded to the AI ​​server for processing before arriving at the NGU (Next Generation User-plane) of the servitized RAN 102 to ensure that the 4K video stream data can be processed in time on the RAN side, thereby reducing the burden on the core network;

[0126] Among them, CUUP is a component in the service-oriented RAN, responsible for processing the transmission of user-plane data. In the architecture of the service-oriented RAN, CUUP, as part of the user plane, performs data encapsulation, transmission and related protocol functions to ensure that data can flow effectively in the wireless network. NGU is a key component for user-plane data transmission in 5G and future networks, responsible for processing user data forwarding, including data encapsulation, transmission and related protocol functions. NGU is integrated in the service-oriented RAN and represents the function of user-plane data transmission. In the actual system architecture, it may be integrated in other user-plane processing units, such as CUUP or other user-plane functional entities. NGU usually works with control plane network elements (such as CUCP, Central Unit Control-plane) to ensure efficient and correct data transmission. In the service-oriented RAN architecture, the role of NGU is to achieve fast forwarding of user-plane data and support a variety of services and application scenarios, such as the transmission of high-definition video streams. It may include caching, processing and optimization of data to meet the quality of service (QoS) requirements of different business needs. That is, NGU usually does not appear as a separate physical entity in the system architecture, but as part of the user plane data processing and transmission functions, it is integrated in user plane network elements such as CUUP to support efficient and flexible data transmission services.

[0127] Setting of the Video Stream Steering (VSS) module: The video stream steering module is integrated in the CUUP uplink transmission processing component. Its main functions include: sending the 4K video stream data packet delivered by the CUUP UL thread (uplink) to the AI ​​server through UDP; configuring the VF DRB identifier through the Telnet protocol; querying the NRF (Network Repository Function) to obtain the network address of the AI ​​server;

[0128] The VSS module is a component specially designed to process high-definition video streams, especially the intelligent diversion and forwarding of 4K video streams. The VSS module is integrated in the uplink transmission processing component of CUUP (Central Unit User-plane). Its main function is to send video stream data to the AI ​​server through the UDP protocol for real-time processing and analysis. NRF is a key component in the 5G network architecture. It is used as part of the service-oriented architecture to support service discovery and registration of network functions (NFs). The main functions of NRF include: 1) Service registration: Each service instance in the network can register its own service information on NRF, including the type, location, interface, etc. of the service. 2) Service discovery: When a service in the network needs to interact with other services, the required service information can be queried through NRF to realize service discovery. 3) Service management: NRF is also responsible for managing the life cycle of the service, including service registration, update, and deregistration. In the service-oriented RAN architecture of this embodiment, NRF enables each network function to flexibly discover and access other services, supporting the implementation of dynamic service orchestration and network slicing in the service-oriented network. Through NRF, the network can respond more flexibly to changes in business needs and achieve optimal configuration and utilization of resources.

[0129] A data adaptation (DA) module is also added to the AI ​​server to process the video stream data transmitted from the video stream forwarding module. The data adaptation module is an independent process that runs on the AI ​​server together with the video server. The main functions of the data adaptation module include: creating a virtual network card on the AI ​​server so that the video analysis application can monitor and process the data received through the virtual network card; adapting the video stream data sent by the video stream forwarding module into data that can be parsed by the AI ​​server and submitting it to the virtual network card; and

[0130] Define the interaction rules between the video stream forwarding module and the CUUP UL thread (UL Task), the video stream forwarding module and the NRF, and the video stream forwarding module and the data adaptation module.

[0131] Figure 3 FIG. 1 is a schematic diagram of the interaction between the video stream forwarding module task and the CUUP UL thread task in an embodiment of the present invention. Figure 3 As shown, in this embodiment, the interaction rules between the video stream forwarding (VSS) module and the CUUP UL thread (UL Task) task are specifically as follows:

[0132] The video stream forwarding module configures the ID of the VF DRB to the CUUP UL thread task. The CUUP UL thread task decides whether to submit the processed data packet to the task of the video stream forwarding module or the UPF (UserPlane Function) of the core network according to the configuration.

[0133] Figure 4 FIG. 1 is a schematic diagram of the interaction between the video stream forwarding module task and the NRF in one embodiment of the present invention. Figure 4 As shown, in this embodiment, the interaction rules between the video stream forwarding (VSS) module and the NRF are specifically as follows:

[0134] The video stream forwarding module queries the IP address of the AI ​​server and the port number of the data adaptation (DA) application from NRF through HTTP requests. The service name registered by the data adaptation application is "videoAnalyzingService".

[0135] Figure 5 FIG. 1 is a schematic diagram of the interaction between the video stream forwarding module task and the data adaptation module in one embodiment of the present invention. Figure 5 As shown, in this embodiment, the interaction rules between the video stream forwarding (VSS) module and the data adaptation (DA) module are specifically as follows:

[0136] The data adaptation module receives data packets of the UDP tunnel from the physical network card (pNIC), i.e., data packets after the service-oriented RAN diverts the video stream data packets uploaded by the terminal device 103 through the tunnel conversion service;

[0137] The data adaptation module obtains the original video stream data packet, that is, the data packet with the UDP+IP header of VSS-DA removed;

[0138] The data adapter module writes the data to the virtual network card (vNIC);

[0139] The video server reads the video stream data from the virtual network card, that is, obtains the video stream data sent to it by the UE.

[0140] Figure 6 FIG. 1 is a schematic diagram of a format for splitting video stream data packets uploaded by a terminal device in one embodiment of the present invention. Figure 7FIG. 1 is a schematic diagram of the format of a video stream data packet pushed by a service-oriented RAN to an AI server in an embodiment of the present invention. Figure 6 and Figure 7 As shown, the data adapter module receives the data packet of the UDP tunnel from the physical network card in the specific format as follows Figure 6 Shown include:

[0141] The uplink video stream data part (the video stream data uploaded by the terminal device), which includes RTSP video data + TCP header (20-40 bytes) or UDP header (8 bytes) + IP header (20-40 bytes), where the source IP and target IP composed of the TCP header or UDP header + IP header are the terminal device IP and the target AI server IP respectively; and

[0142] The UDP header (8 bytes) + IP header (20-60 bytes) added by the service-oriented RAN, the source IP and target IP of which are the service-oriented RAN IP and the tunnel switching service IP respectively;

[0143] The format of the video data packets pushed by the service-oriented RAN to the AI ​​server at the edge is as follows: Figure 7 As shown, only the uplink video stream data portion is included, and the header portion of the service-oriented RAN IP and the tunnel switching service IP is removed.

[0144] Figure 8 FIG. 1 is a flow chart of a service-oriented RAN discovering a local AI service in an embodiment of the present invention. According to the above description, the local AI service 105 is discoverable. After the service-oriented RAN discovers the AI ​​service, the 4K streaming video is diverted to the local network. Figure 8 As shown, in this embodiment, the specific process of the service-oriented RAN 102 discovering the local AI service 105 includes:

[0145] Step S101: The tunnel switching service 106 in the service-oriented RAN 102 registers services with the RNRF module (Radio Network Repository Function, which is a network service discovery and subscription module) in the service-oriented RAN 102 through the HTTP interface. Specifically, the tunnel switching service 106 sends a service registration request to the RNRF module, and the RNRF module returns a service registration response to the tunnel switching service 106;

[0146] Step S102: After the local AI service 105 is started, the local AI service 105 registers services with the RNRF module in the service-oriented RAN through the HTTP interface;

[0147] Step S103: The service-oriented RAN 102 obtains the service name and IP address / port information of the local AI service 105, and replies with a service registration response; wherein, in this embodiment, when registering a service, <service name>—<IP&port> The mapping between them is similar to DNS mapping, so I will not go into details;

[0148] Step S104: the service-oriented RAN 102 queries the health status of the local AI service 105 according to a preset timing;

[0149] Step S105: When the device terminal 103 initiates a query of the local AI service 105, the query is sent to the core network 101 through the service-oriented RAN 102, and the core network returns the query result to the device terminal 103 through the service-oriented RAN 102;

[0150] Among them, under the premise of distributed deployment of services (i.e., each service instance runs as an independent process), the address of the service instance may change dynamically during the process of service instance startup, deletion, migration, etc. In order to ensure mutual communication between services, a centralized service registration center is established to provide service discovery functions for other services. The RNRF module provided by the present invention is a network service discovery and subscription module. The RNRF module (Radio Network Repository Function) is used to support service discovery and subscription, and the RNRF module is implemented based on the open source software Consul.

[0151] Fig.12 FIG. 1 is a schematic diagram of RNRF module service discovery in one embodiment of the present invention. Fig.12 As shown in the figure, service instances 1 and 2 are both clients of the service registration center and register services with the service registration center. Service instance 2 queries the service registration center for the network address of service instance 1 and then initiates a business request to service instance 1. The specific process is as follows:

[0152] Fig.13 FIG. 1 is a schematic diagram of a service discovery and subscription process of an RNRF module in an embodiment of the present invention. Fig.13 As shown in the figure, the service discovery and subscription process of the RNRF module is as follows:

[0153] Step S1101: serv1 (service instance 1) initiates a registration request to serv_reg (service registration center). serv_reg (service registration center) maintains the metadata of serv1 (service instance 1) and starts health monitoring;

[0154] Step S1102: serv_reg (service registration center) replies the registration result to serv1 (service instance 1), along with the heartbeat port;

[0155] Step S1103: serv2 (service instance 2) initiates a registration request to serv_reg (service registration center). serv_reg (service registration center) maintains the metadata of serv2 (service instance 2) and starts health monitoring;

[0156] Step S1104: serv_reg (service registration center) replies the registration result to serv2 (service instance 2), along with the heartbeat port;

[0157] Step S1105: serv2 (service instance 2) subscribes to the service of serv1 (service instance 1) from serv_reg (service registration center), and sends serv_name (service instance name). serv_reg (service registration center) maintains the subscription information of serv2 (service instance 2), and pushes it to serv2 (service instance 2) when the data subscribed by serv2 (service instance 2) changes;

[0158] Step S1106: serv_reg (service registration center) pushes the IP address and port of serv1 (service instance 1) to serv2 (service instance 2) for the first time;

[0159] Step S1107: serv2 (service instance 2) sends a service request to serv1 (service instance 1);

[0160] Step S1108: serv1 (service instance 1) feeds back a service response to serv2 (service instance 2).

[0161] Fig.14 FIG. 1 is a schematic diagram of the RNRF module service registration process in one embodiment of the present invention. Fig.14 As shown in the figure, the service registration process of the RNRF module is as follows:

[0162] After serv_reg (service registration center) is started and the interface is initialized,

[0163] Step S1201: Any service instance (serv) sends a registration request, including information such as serv_name (service name), ip_addr (IP address), serv_port (port), etc.;

[0164] Step S1202: serv_reg (service registration center) receives the request and gives a response whether the registration is successful:

[0165] If the registration is successful, serv_reg (service registration center) feeds back the heartbeat signal interface information to the service instance (serv);

[0166] If the registration fails, serv_reg (service registration center) will feedback the reason for the registration failure to the service instance (serv).

[0167] Fig.15 FIG. 1 is a schematic diagram of a RNRF module service subscription / query process in an embodiment of the present invention. Fig.15 As shown, the service subscription and query process of the RNRF module is as follows:

[0168] Step S1301: Any service instance subscribes to the network address of serv_name (service name) from the service registration center:

[0169] If the service registration center has subscribed service information, the service instance is added to the subscription list of serv_name (service name);

[0170] If the service registration center does not have the subscribed service information, the feedback query fails;

[0171] Step S1302: When the service registration center receives the address information modification request of serv_name (service name), it traverses the subscription list of serv_name (service name) to modify it;

[0172] Step S1303: The service registration center publishes the address information change of serv_name (service name).

[0173] Fig. 9 The figure is a schematic diagram of the processing flow of 480P video stream data in one embodiment of the present invention. Since the 480P video stream data is mainly analyzed and processed by the cloud server, the present invention uses a dedicated non-4K video prototype service-oriented RAN system for the 480P video stream data, and uploads the non-4K video stream collected by the terminal device to the cloud core network through the service-oriented RAN. This method belongs to the undiscovered local AI service, such as Fig. 9 As shown, in this embodiment, the shunting process of 480P video stream data is specifically as follows:

[0174] Step S201: the service-oriented RAN queries the local AI service and adopts the service-oriented RAN protocol stack for non-4K video streams;

[0175] Step S202: After the terminal device accesses the wireless network, the terminal device queries the network for local AI services;

[0176] Step S203: The camera of the terminal device collects video stream data;

[0177] Step S204: the terminal device does not find the local AI service, and the terminal device sends the collected non-4K video stream data (such as 480P) to the service-oriented RAN;

[0178] Step S205: the service-oriented RAN receives the uplink video stream data service from the terminal device, and pushes the video stream data to the video application server through the core network.

[0179] Fig.10 The figure is a schematic diagram of the offloading processing flow of 4K video stream data in one embodiment of the present invention. For the 4K video stream data service, the present invention adopts a dedicated 4K video prototype service-oriented RAN system. The 4K video stream data collected by the terminal, the service-oriented RAN matches the video data that meets the characteristics of the local AI service during the service transmission process, and pushes it to the local AI service, such as Fig.10 As shown, in this embodiment, the shunting process of 4K video stream data is specifically as follows:

[0180] Step S301: The service-oriented RAN discovers the local AI service in the system, queries the metadata of the local AI service, including the service name and IP address / port information, and switches to the service-oriented RAN protocol stack dedicated to 4K video streaming;

[0181] Step S302: After the terminal device accesses the wireless network, the terminal device queries the network for local AI services;

[0182] Step S303: The camera of the terminal device collects video stream data;

[0183] Step S304: After the terminal device queries the local AI service, the terminal device sends the collected video stream data to the service-based RAN;

[0184] Step S305: After receiving the uplink video stream data service from the terminal device and identifying the local diversion identifier (such as VF DRB dedicated to 4K video stream), the service-oriented RAN performs local IP network diversion on the uplink video stream data of the terminal device, specifically, pushes the video stream data to the tunnel conversion service through the UDP transmission protocol;

[0185] Step S306: The tunnel conversion service performs tunnel relationship adaptation on the diverted video stream data, and pushes the video stream data to the target local AI service;

[0186] Step S307: The local AI service performs corresponding intelligent analysis on the uplink video stream data.

[0187] Fig.11This is a schematic diagram of service arrangement for video scenarios in one embodiment of the present invention. The present invention arranges services for two application scenarios: 480P video stream cloud analysis and 4K video stream edge analysis. Assume a scenario where a 480P dedicated protocol stack switches to a 4K dedicated protocol stack. Assume that the wireless network in a certain area only needs to serve the 480P video cloud AI analysis scenario. At this time, according to the instructions of the network operator or network service manager, the wireless network in this area needs to serve the 4K video AI analysis scenario. In order to reduce the bandwidth pressure of the backhaul network, a 4K dedicated wireless protocol stack needs to be arranged. Fig.11 As shown, this embodiment provides a service orchestration method for video streaming, which is implemented by the aforementioned dedicated wireless protocol stack and specifically includes:

[0188] S1. The decision control function (PCF) of the service-oriented RAN initiates a request to the computing plane control function (a function implemented by the PCF in the service-oriented RAN) to launch the AI ​​analysis service for edge video stream data.

[0189] S2. The computing surface control function feeds back the computing resource scheduling results to the decision control function;

[0190] S3. The decision control function initiates a service request to the service scheduling function (SSF) to schedule the service-oriented RAN protocol stack dedicated to 4K video streaming and the AI ​​analysis service of edge video streaming data;

[0191] S4. The service scheduling function pulls up a service-oriented RAN protocol stack function instance dedicated to 4K video streaming (service-oriented RAN control plane and user plane services);

[0192] S5. The service scheduling function launches the AI ​​analysis service for edge video stream data.

[0193] In summary, the present invention designs a service-oriented RAN protocol stack dedicated to 4K video streams, including VideoForwarding DRB (VF DRB) and a video stream forwarding module (VSS). VF DRB effectively diverts and processes 4K video stream data on the RAN side, and the VSS module is responsible for sending the video stream data to the AI ​​server through the UDP protocol to achieve real-time analysis and processing. The present invention proposes an interaction mechanism between a service-oriented RAN and a local AI service. Through the service management network element, the service-oriented RAN can automatically discover and register local AI services, and flexibly select processing paths in different scenarios to ensure the real-time performance and processing efficiency of the system. In response to the needs of video streams with different resolutions, the present invention provides a dynamically switching service orchestration method that can switch between 480P video stream cloud analysis and 4K video stream edge analysis. The system starts or shuts down the corresponding service instance according to actual needs to achieve the optimal configuration of resources. By designing a dedicated 4K video stream local diversion processing process, the present invention can identify and process 4K video streams, reduce long-distance data transmission, and reduce latency. The collaboration between the data adapter module (DA) and the VSS module ensures the efficiency and stability of the video data transmission process from the physical network card to the virtual network card.

[0194] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0195] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dedicated wireless protocol stack for video streaming, which is constructed as a cloud-edge-end system architecture, characterized in that: It includes core network, service-oriented RAN and terminal equipment, including: The core network is deployed in the cloud, connected to a video application server also deployed in the cloud, and is responsible for processing and storing video stream data from the terminal; The service-oriented RAN is deployed at the edge of the network to provide wireless access services for the terminal device, wherein the service-oriented RAN includes a service management network element and a local AI service also deployed at the edge of the network, the local AI service runs on an AI server, and the service-oriented RAN dynamically discovers and utilizes the local AI service through the service management network element to process the intelligent analysis task of the 4K video stream; The terminal device is wirelessly connected to the service-oriented RAN, and uploads the video stream data collected by its camera through the service-oriented RAN using different processing strategies according to different resolutions, specifically: For 4K video streams: when the service-oriented RAN discovers the local AI service, the 4K video stream collected by the terminal device is diverted to the local AI service deployed at the edge of the network for processing; For 480P video stream: the service-oriented RAN directly transmits the 480P video stream uplink through the core network to the video application server deployed in the cloud for intelligent analysis and processing.

2. The dedicated wireless protocol stack for video streaming according to claim 1, characterized in that: The service-oriented RAN adopts the service-oriented and modular design principles to decompose the traditional radio access network functions into independent service components, which also include: Service-oriented RAN control plane: including CUCP: responsible for radio resource management, session management and mobility management control functions; Service-oriented RAN user plane: including CUUP: responsible for processing the transmission of user data, including data encapsulation, encryption and compression; Data flow distribution service: responsible for distributing user plane data to different processing paths; Service orchestrator: responsible for dynamically orchestrating and optimizing service instances based on business needs and network policies; Decision-making and control function: Make decisions on starting, stopping, scaling up or down service instances based on network policies and business needs; Digital object management function: responsible for managing the status and configuration of digital objects in the network; Service scheduling function: Responsible for the specific scheduling and scaling operations of service instances based on the decisions made by the decision control function; Network Function Service Interface: responsible for defining the communication protocols and interfaces between services, and supporting the interoperability and modularity of services; Tunnel conversion service: responsible for establishing and maintaining data transmission tunnels between the user plane and edge computing nodes; Video stream forwarding module: responsible for sending video stream data packets to the local AI server via UDP to achieve local diversion and processing of video streams; Service-oriented RAN prototype verification system: used to test and verify the functions of the service-oriented RAN architecture and components, supporting end-to-end business processes, including a dedicated 4K video prototype service-oriented RAN system and a dedicated non-4K video prototype service-oriented RAN system.

3. The dedicated wireless protocol stack for video streaming according to claim 2, characterized in that: The service-oriented RAN prototype verification system defines a radio bearer mechanism for intelligent analysis of large video streams, namely VF DRB. The implementation of the radio bearer mechanism specifically includes: DRB configuration and data forwarding settings: When the user's terminal device accesses the network, the CUUP of the service-oriented RAN checks whether the DRB to which the uploaded data packets belong is a VF DRB. If so, the data packets are forwarded to the AI ​​server for processing before reaching the NGU of the service-oriented RAN; Setting of video stream forwarding module: The video stream forwarding module is integrated in the CUUP uplink transmission processing component. Its main functions include: sending the 4K video stream data packet submitted by the CUUP UL thread to the AI ​​server through UDP; configuring the VF DRB identifier through the Telnet protocol; querying NRF to obtain the network address of the AI ​​server; A data adaptation module is also added to the AI ​​server to process the video stream data transmitted from the video stream forwarding module. The data adaptation module is an independent process that runs on the AI ​​server together with the video server. The main functions of the data adaptation module include: creating a virtual network card on the AI ​​server so that the video analysis application can monitor and process the data received through the virtual network card; adapting the video stream data sent by the video stream forwarding module into data that can be parsed by the AI ​​server and submitting it to the virtual network card; and Define the interaction rules between the video stream forwarding module and CUUP UL thread tasks, the video stream forwarding module and NRF, and the video stream forwarding module and data adaptation module.

4. The dedicated wireless protocol stack for video streaming according to claim 3, characterized in that: The interaction rules between the video stream forwarding module and the CUUP UL thread task are as follows: The video stream forwarding module configures the ID of the VF DRB to the CUUP UL thread task. The CUUP UL thread task decides whether to submit the processed data packet to the task of the video stream forwarding module or the UPF of the core network based on the configuration.

5. The dedicated wireless protocol stack for video streaming according to claim 3, characterized in that: The interaction rules between the video stream forwarding module and NRF are as follows: The video stream forwarding module queries NRF for the IP address of the AI ​​server and the port number of the data adaptation application through HTTP requests.

6. The dedicated wireless protocol stack for video streaming according to claim 3, characterized in that: The interaction rules between the video stream forwarding module and the data adaptation module are as follows: The data adapter module receives data packets of the UDP tunnel from the physical network card, i.e., data packets after the service-oriented RAN diverts the video stream data packets uploaded by the terminal device through the tunnel conversion service; The data adaptation module obtains the original video stream data packet; The data adapter module writes the data into the virtual network card; The video server reads the video stream data from the virtual network card.

7. The dedicated wireless protocol stack for video streaming according to claim 3, characterized in that: The specific process of service-oriented RAN discovering local AI services includes: Step S101: The tunnel switching service in the service-oriented RAN registers the service with the RNRF module in the service-oriented RAN through the HTTP interface. Specifically, the tunnel switching service sends a service registration request to the RNRF module, and the RNRF module returns a service registration response to the tunnel switching service. The RNRF module is a network service discovery and subscription module for supporting service discovery and subscription. Step S102: After the local AI service is started, the local AI service registers the service with the RNRF module in the service-oriented RAN through the HTTP interface; Step S103: The service-oriented RAN obtains the service name and IP address / port information of the local AI service, and replies with a service registration response; Step S104: the service-oriented RAN queries the health status of the local AI service according to a preset timing; Step S105: When the device terminal initiates a query for a local AI service, it is sent to the core network via the service-based RAN, and the core network returns the query result to the device terminal 103 via the service-based RAN.

8. The dedicated wireless protocol stack for video streaming according to claim 3, characterized in that: A dedicated non-4K video prototype service-oriented RAN system is used to offload 480P video stream data. The non-4K video stream collected by the terminal device is uploaded to the cloud core network through the service-oriented RAN. The specific process is as follows: Step S201: the service-oriented RAN queries the local AI service and adopts the service-oriented RAN protocol stack for non-4K video streams; Step S202: After the terminal device accesses the wireless network, the terminal device queries the network for local AI services; Step S203: The camera of the terminal device collects video stream data; Step S204: the terminal device does not find the local AI service, and the terminal device sends the collected non-4K video stream data to the service-based RAN; Step S205: the service-oriented RAN receives the uplink video stream data service from the terminal device, and pushes the video stream data to the video application server through the core network.

9. The dedicated wireless protocol stack for video streaming according to claim 3, characterized in that: A dedicated 4K video prototype service-oriented RAN system is used to offload 4K video stream data. The specific process is as follows: Step S301: The service-oriented RAN discovers the local AI service, queries the metadata of the local AI service, including the service name and IP address / port information, and switches to the service-oriented RAN protocol stack dedicated to 4K video streaming; Step S302: After the terminal device accesses the wireless network, the terminal device queries the network for local AI services; Step S303: The camera of the terminal device collects video stream data; Step S304: After the terminal device queries the local AI service, the terminal device sends the collected video stream data to the service-based RAN; Step S305: the service-oriented RAN receives the uplink video stream data service from the terminal device, and after identifying the local diversion identifier, performs local IP network diversion on the uplink video stream data of the terminal device, specifically, pushes the video stream data to the tunnel conversion service through the UDP transmission protocol; Step S306: The tunnel conversion service performs tunnel relationship adaptation on the diverted video stream data, and pushes the video stream data to the target local AI service; Step S307: The local AI service performs corresponding intelligent analysis on the uplink video stream data.

10. A video stream-oriented service orchestration method, which is implemented by the dedicated wireless protocol stack according to any one of claims 3 to 9, characterized in that: Specifically include: S1. The decision control function of the service-oriented RAN initiates a request to the computing plane control function to launch the AI ​​analysis service for edge video stream data. S2. The computing surface control function feeds back the computing resource scheduling results to the decision control function; S3. The decision control function initiates a request to the service scheduling function to schedule the service-oriented RAN protocol stack dedicated to 4K video streaming and the AI ​​analysis service of edge video streaming data; S4. The service scheduling function pulls up a service-oriented RAN protocol stack function instance dedicated to 4K video streaming; S5. The service scheduling function launches the AI ​​analysis service for edge video stream data.

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