94 results about "Differentiated services" patented technology

The invention relates to the technical field of voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

A routing method for supporting differential service requirements in a satellite internet comprises the following steps: step 1, constructing multi-dimensional network state perception as a base layer which is responsible for collecting and quantifying physical states and logic states of a satellite network in real time, and obtaining objective network states and subjective QoS (Quality of Service) requirements of services; 2, generating a nonlinear adaptive weight driven by a service as a strategy layer, mapping an objective network state and a subjective QoS (Quality of Service) demand of the service, dynamically adjusting the value orientation of routing judgment, and generating a dynamic weight; and step 3, taking the dynamic cost routing decision as an execution layer, calculating the comprehensive cost based on the dynamic weight, and executing next hop selection and path maintenance, and through cooperative work of the dynamic cost routing decision, the comprehensive cost and the next hop selection and path maintenance, realizing intelligent path selection and high-reliability transmission for different service flows in a high-dynamic and resource-limited environment of the low-orbit satellite. According to the invention, while the routing computation overhead is effectively reduced, multiple requirements of differentiated services on low time delay and high reliability can be considered.

The invention discloses an AI dynamic game virtualization resource scheduling system and method for a 5G low-delay service, and aims to solve the problems of insufficient QoS guarantee and low resource utilization rate caused by insufficient static rule adaptability, lack of service differentiation scheduling and low fusion degree of a game model and AI. A service feature extraction module is used for distinguishing a low-delay service (T = 0) and a common service (T = 1), constructing an AI dynamic game model taking the two types of services as participants, defining a payment function comprising a QoS satisfaction function and a resource consumption cost function, solving Nash equilibrium by adopting a deep reinforcement learning algorithm, outputting an optimal resource demand strategy, and realizing the optimal resource demand strategy. And the bandwidth and CPU allocation are dynamically adjusted through the virtualized resource management module. According to the invention, the delay compliance rate of the low-delay service can be improved by 20%-30%, the resource utilization rate of the common service can be improved by 15%-25%, the strategy iteration period is shortened to millisecond level, and differential fine scheduling and dynamic optimization in a 5G mixed service scene are realized.

The invention provides an end-network cooperative transmission system and method for a multi-mode network. The system comprises a terminal service demand layer which is used for identifying the service type of a terminal in a multi-mode network, generating structured service demand information and transmitting the service demand information to a network collaborative decision-making layer; the network cooperation decision-making layer is used for fusing the service demand information and the link state information, generating a transmission optimization strategy including path selection, priority mapping, bandwidth allocation and rate control, and transmitting the transmission optimization strategy to the transmission optimization strategy execution layer; and the transmission optimization strategy execution layer is used for executing a transmission optimization strategy, collecting link state information in the multi-mode network in real time through INT, and transmitting the link state information to the network cooperation decision layer. According to the method, efficient scheduling of heterogeneous network resources and high-reliability transmission guarantee of differentiated services are achieved, and the method is suitable for key service scenes sensitive to time delay and reliability, such as Internet of Vehicles and ultra-high-definition video transmission.

The invention relates to the technical field of large language models, in particular to a high-concurrency large language model high-speed reasoning deployment method, which comprises the following steps of: extracting a weight coefficient corresponding to a user identity identifier, an emergency degree numerical value mapped by a request type and a resource occupation value converted by an estimated calculation amount according to the user identity, the request type and the estimated calculation amount; according to the method, the dynamic priority is generated by performing comprehensive operation on the user identity, the request type and the estimated calculation amount, so that differentiated services for reasoning tasks are realized, interactive requests with high urgency degree can bypass batch processing tasks with long time consumption, the response delay and delay jitter of key services are reduced, and the user experience is improved. Meanwhile, according to the complexity of the request text and the geographic coordinates of the user, the task is intelligently routed to the model node with the most suitable scale in the distributed network, so that the wide area network transmission delay is greatly reduced through edge processing, and the computing power waste caused by using a super-large-scale model to process the simple task is also avoided.

The invention discloses a multi-tenant resource demand prediction method based on an LSTM-XGBoost fusion model. The multi-tenant resource demand prediction method comprises the following steps: constructing multi-dimensional input characteristics including a tenant historical resource usage amount, a task priority, an SLA constraint coefficient and a time period load mode; respectively inputting the input features into an LSTM (Long Short Term Memory) time sequence channel and an XGBoost attribute channel for feature extraction; splicing the feature vectors output by the two channels, and inputting the spliced feature vectors to a fusion layer for joint modeling; and outputting the resource demand prediction vector in the future time period through a regression device. By introducing a dual-channel modeling structure, structured information such as historical resource usage sequences and task priorities of tenants, SLA constraint coefficients and load modes is processed respectively, and accurate prediction of multi-dimensional virtual resource demands is realized. By combining a fusion layer and a regression output mechanism, the prediction precision before resource scheduling is improved, efficient utilization and fair distribution of platform resources are realized, and differentiated service guarantee requirements in a multi-tenant environment are met.

The invention relates to the technical field of computers, and discloses a process scheduling method based on a tenant isolation strategy, and the method comprises the steps: receiving a process scheduling request of a tenant, and carrying out the standardization processing and legality verification; querying a predefined tenant mapping table, and determining an internal unified tenant identifier corresponding to the flow scheduling request and associated single-tenant concurrent quota, tenant resource weight, isolation level and strategy rule; obtaining a single-tenant concurrent quota and a tenant resource weight, generating a tenant isolation strategy in combination with the calculated rate limiting parameter and the tenant correction priority, and performing admission judgment; and based on the judgment result, distributing the flow scheduling request to the corresponding tenant scheduling unit according to the local priority, selecting a target tenant scheduling unit according to the scheduling qualification value and the global scheduling priority, generating an execution instance and triggering the execution instance. Through differentiated services, resource monopoly is prevented, task hunger is eliminated, system overload is avoided, multi-dimensional mutual restriction is carried out, and isolation between tenants is achieved.

The invention discloses a performance monitoring system and method for expansion and upgrading of a big data platform, and relates to the technical field of big data analysis. According to the performance monitoring system and method, key information of newly-added Web servers is collected to form a standardized configuration data set, and node deployment normalization and cluster compatibility in the expansion stage are improved; operating load logs before capacity expansion are directionally pulled and screened, and a historical cluster server operating load set is constructed through structural analysis; first dynamic weight configuration is completed by combining two types of load sets, the cache types of the Web servers are distinguished according to cache configuration identifiers, and secondary weight adjustment is performed on the shared distributed cache Web servers based on the hit rate, so that the defects of existing weight configuration are overcome, accurate weight adaptation is realized, the cache hit rate is increased, the frequency of requesting to penetrate through a database is reduced, and the service life of the Web servers is prolonged. The database load is reduced, the response delay is shortened, resource imbalance and service instability are avoided, and the performance of platform expansion upgrading operation is guaranteed.

The invention discloses an urban public facility layout optimization method and system based on multi-source space-time big data, and relates to the technical field of urban planning. Comprising the following steps: firstly, acquiring and fusing multi-source space-time big data such as mobile equipment signaling data and point-of-interest data; identifying and delimiting an urban public service three-level center system based on the fusion data through a spatial clustering and network analysis algorithm, and determining each center level and a spatial influence range; constructing a differentiated service demand quantification model for each level of service center; constructing a multi-stage collaborative layout optimization model, and completing facility hierarchical assignment and site selection optimization; evaluating the layout scheme in the simulation environment and outputting a grading evaluation report; through dynamic analysis and multi-level collaborative optimization driven by multi-source space-time big data, accurate matching of public facility layout and service requirements is achieved, and meanwhile the service coverage rate and cross-level connection efficiency are improved.

The embodiment of the invention provides an intelligent customer service response method and device, electronic equipment and a medium, belongs to the technical field of artificial intelligence, and is suitable for financial scenes and medical scenes. The method comprises the following steps: performing service period prediction by combining user portrait features and behavior features extracted from service interaction data to obtain a user service period state; performing product demand prediction according to dialogue features, behavior features, user portrait features and user service period states extracted from the service interaction data to obtain a product service demand degree; and sending a target service response scheme generated based on the product service demand degree and the user service cycle state to the user. According to the embodiment of the invention, the target service response scheme is generated by combining the product service demand degree and the user service period state generated by the dialogue features, the behavior features and the portrait features of the user, differentiated service strategies can be adopted for clients with different demand degrees in a proper service stage, and the intelligent customer service response effect is improved.

The invention provides a smart campus task collaboration method and system oriented to multi-group service optimization, and relates to the technical field of campus service management. According to the method, historical service logs are called, and storage nodes are distributed according to a service process to generate data sets before and after collaboration; determining a group cooperation state based on the data set, and constructing an exclusive task measurement set and a group behavior feature set; quantifying a cross-group task cooperation priority through a double-evaluation mode mark; generating a group-task collaborative set in combination with iterative operation, and screening and optimizing by using a segmentation coefficient (cohesion + separation degree); the system correspondingly comprises a data acquisition and storage module, a task and feature construction module, a collaborative priority quantification module and a collaborative adaptation analysis and optimization module. According to the method, the problems of lack of precise adaptation, fuzzy priority quantization and the like in multi-group coordination in the prior art are solved, directional optimal configuration of service resources is realized, task coordination efficiency and group satisfaction are improved, and an efficient solution is provided for multi-group differentiated services of the smart campus.

The invention belongs to the technical field of wireless communication, discloses a perception-assisted satellite-borne base station access method, device and equipment and a storage medium, and is applied to a non-ground network satellite access system. The method comprises the following steps: firstly, constructing a multi-dimensional information sensing model oriented to differentiated service requirements, fusing user requirement information, network state information and satellite-ground channel information, and monitoring the use condition of a resource block in real time; the method comprises the following steps: carrying out real-time estimation on the number of active users based on an active user number estimation method of a Bayesian criterion and multi-dimensional perception information, and then, carrying out self-adaptive allocation of time-frequency resources on the users by adopting a weighted dynamic resource allocation strategy according to service priorities and weights; and the access control parameters are dynamically adjusted according to the number of the active users and the resource allocation condition, so that efficient and reliable access in a high-dynamic and frequent switching scene is realized. According to the invention, the resource utilization rate and the access success rate of the satellite access system can be obviously improved, and the method adapts to differentiated service requirements in a high-dynamic scene.

The application discloses an intelligent traffic arrangement method based on a fusion of an expert network and deep reinforcement learning, comprising the following steps: collecting network node and link state data in real time, and constructing a time sequence input vector and a topological graph structure; using a time sequence neural network and a graph neural network to respectively extract traffic space-time features and node topological features, fusing the features, predicting future traffic through a classification network, and completing coarse-grained arrangement to different service level network slices; modeling resource scheduling as a multi-agent Markov decision process, designing a state space, an action space and a reward function; initializing a deep reinforcement learning agent, and training the agent through interactive experience; fusing a pre-trained expert strategy network, constructing a total loss function to optimize network parameters; and finally generating an intelligent strategy capable of dynamically optimizing traffic paths and resource allocation according to real-time states; and the application can realize efficient resource scheduling under differentiated service quality requirements of multiple services.

The invention provides a tunnel network differential service implementation method and device and network equipment, aiming at an IP message entering a tunnel network from a user network, an incoming node on the tunnel network in an incoming direction searches a data association table based on the IP message to obtain a configuration parameter; and under the condition that the configuration parameter represents that the differential service mode of the tunnel network is a pipeline mode or a short pipeline mode, querying an editing table based on the IP message to obtain EXP distribution information. And determining the internal priority of the IP message based on the EXP allocation information, and performing scheduling processing on the IP message based on the internal priority of the IP message in the tunnel network. According to the scheme, in the pipeline mode or the short pipeline mode, the internal priority can be obtained based on the EXP allocation information configured in the exit direction of the incoming node, so that queue scheduling on the node is influenced by the EXP allocation information on the node, and the priority management requirement of a user on the node is met.

A communication method and related apparatus, in which a first communication device receives first information indicating N gateway stations and / or K feeder links on one or more transmission paths of data of a terminal device, and the first communication device can determine a first user plane function connected with the N gateway stations and / or the K feeder links according to the first information. The first communication device can subsequently send second information to the first user plane function, so that the first user plane function can perform data processing based on the N gateway stations and / or the K feeder links indicated by the second information. In this way, the first communication device can select a user plane function based on the gateway stations and / or feeder links indicated by the first information, so that the data of the terminal device can be transmitted through the user plane function and the gateway stations matching the first information, to provide differentiated services and improve the quality and reliability of the services.

The invention discloses an air-sea cross-domain communication network slice dynamic mapping and forwarding method based on an SRv6 Color-Mapping table, and belongs to the technical field of communication networks, and the method comprises the steps: constructing a cross-domain network intelligent management and control platform, defining a network slice service level according to a service demand, calculating an optimal SRv6 strategy path meeting a constraint condition, and transmitting the optimal SRv6 strategy path to the cross-domain network intelligent management and control platform; creating an SRv6 Color-Mapping table of an ordered SID list corresponding to a service grade, an SRv6 color value and an optimal strategy path; the network entry node identifies the service level of the service data packet and marks the corresponding color value of the service data packet, performs SRv6 packaging according to the SID list associated with the color value, and forwards the service data packet segment by segment according to the SID list to complete end-to-end cross-domain communication; and the management and control platform monitors the network state in real time, dynamically updates the mapping table and issues the mapping table, so that non-perception path switching of the service flow is realized. According to the invention, non-aware switching and automatic management of services are realized, and differentiated services and network reliability in a complex heterogeneous environment are effectively guaranteed.

The application provides a kind of star-ground fusion network differentiated service flow deterministic transmission scheduling method and system, belongs to communication network technical field, according to the total demand of service flow, based on the elastic transmission algorithm of cache utilization of genetic, calculate the complementary star-ground cross-domain peak cache resource reserved, and carry out the adaptive optimization of cache resource in domain, obtain the domain node resource allocation result;According to node resource allocation result, based on the transient routing and variable queue algorithm of deep reinforcement learning, the two-dimensional scheduling of routing-queue is carried out to service flow, and deterministic transmission scheduling is carried out to meet the differentiated cache capacity demand and time demand of service flow.The application makes up the defects of traditional STIN network architecture, such as lack of differentiated deterministic service concept and loose adaptation between protocol stack levels, significantly improves the deterministic communication guarantee capability of network in different scenarios, and provides strong support for efficient operation and reliable transmission of STIN.

The invention relates to the technical field of medical data processing, and discloses an intelligent digital operating room data processing method and system based on the 5G Internet, and the method comprises the steps: building a URLLC slice and an eMBB slice, configuring differentiated service quality parameters, and completing the end-to-end deployment; executing real-time registration of the intraoperative image and the preoperative three-dimensional image on the edge computing node; the multi-channel physiological signals are analyzed in real time, and hierarchical control instructions are generated; privacy area detection is carried out on the operation view video, and a shielding mask is generated and synthesized in real time; transmitting the control instruction to a target terminal device through a URLLC slice, and transmitting the shielded video to a video receiving terminal through an eMBB slice; federal learning is realized by adopting homomorphic encryption and differential privacy technologies, and original medical data is ensured not to go out of an operating room boundary. Real-time processing and safe transmission of operating room data are achieved, and operation safety and teaching demonstration efficiency are remarkably improved.

Differentiated service in a federation-based access network is provided by receiving a set of credentials from a User Equipment (UE) for a wireless network offering a plurality of service levels. In response to determining that the set of credentials indicate a realm associated with a given service level, network access is provided to the UE according to the given service level. In response to determining that the given service level is not a highest service level in the wireless network, a list of one or more preferred realms is transmitted to the UE, where each realm of the list of one or more preferred realms is associated with one or more higher service levels than the given service level.

The invention provides a computing network collaborative differentiated resource guarantee system based on a service type, and the system comprises a service type recognition module which is configured to perform service type recognition according to at least one service request, and obtain a target service type and / or a normalized service portrait corresponding to the service request; the differentiated resource strategy generation module is configured to generate a target unified strategy according to the target service type and / or the normalized service portrait, and the target unified strategy comprises computing power resource parameters and network resource parameters corresponding to the service request; and the computing network joint admission module is configured to judge whether the service request is admitted to a target resource domain or not according to the target unified strategy. Through the system, the problems of low resource management efficiency and lack of uniformity for differentiated services in the prior art are solved.

The invention provides an AI flow transmission method and device, a computer readable medium and electronic equipment, the method is executed by a base station node, and the method comprises the following steps: determining a service data intelligent identifier matched with an AI service data flow, and adding the service data intelligent identifier to the AI service data flow to obtain a new AI service data flow; aI QoS flow configuration information and a service data intelligent identifier obtained from a core network side are reported to a wireless access network intelligent layer, and the wireless access network intelligent layer determines a differentiated service guarantee strategy corresponding to a new AI service data flow according to the AI QoS flow configuration information and the service data intelligent identifier and returns the differentiated service guarantee strategy; and determining a transmission path of the AI QoS flow based on the wireless access network intelligent layer according to the differentiated service guarantee strategy and available resource information obtained from a base station network management system, and transmitting the AI QoS flow. The invention provides a transmission scheme which is more adaptive to the AI flow.

The invention provides a ship lock operation scheduling system and method based on big data analysis, and relates to the technical field of ship lock operation scheduling. By integrating big data analysis of historical operation data, ship priority evaluation, real-time navigation state monitoring and intelligent prediction, refinement, dynamics and intelligence of ship lock scheduling are achieved, the overall traffic efficiency is remarkably improved, and congestion of a lock area is effectively relieved. Low-efficiency links are identified through clustering analysis, congestion points are captured in real time in combination with a space thermodynamic diagram, and a declaration time period and interval time are dynamically adjusted, so that the adaptive capacity of the system to abnormal conditions such as load change, severe weather and equipment faults is enhanced; differentiated services and green channel support of high-priority ships are realized, and key transportation requirements are guaranteed; and upstream and downstream ship lock linkage scheduling is realized through a network cloud platform, and a neural network model is utilized to generate an optimization scheme containing a lockage sequence, suggested arrival time and a coordination instruction to improve watershed navigation collaboration.

The invention belongs to the technical field of wireless communication, and particularly relates to a self-adaptive backoff method based on emergency degree and channel load volatility. According to the method, the survival time constraint of the data packet, the same-priority differentiation demand and the dynamic change characteristic of the channel load are comprehensively considered. By sensing the time emergency degree of a data packet, shorter back-off time is allocated to the data packet which is close to timeout, and meanwhile, differential control in the same priority is realized; the backoff strategy is flexibly adjusted in a dynamic environment by monitoring the channel load fluctuation condition. The multi-dimension (data packet priority, channel occupancy rate, data packet emergency degree and channel load volatility) adaptive mechanism can reduce the system time delay and packet loss rate, improve the channel utilization rate and the system throughput, and enhance the fairness and differentiated service effect among data with different priorities while ensuring the high-priority data transmission performance.

This invention belongs to the field of communication and network control technology. To address the problems of poor adaptability in non-ideal observation environments and the inability of a single optimization objective to meet various differentiated service requirements, and to overcome scheduling failures caused by network jitter or node failures leading to data loss, this invention provides: a time-sensitive network traffic scheduling method based on causal data completion. This method collects real-time traffic data from the current time-sensitive network (TSN) and constructs an irregular observation matrix; it uses a state reconstruction module based on neural Granger causality to complete the missing data in the observation matrix; it extracts robust spatiotemporal features with noise resistance from the completed observation matrix using a variational graph attention network; and it inputs these robust spatiotemporal features into a reinforcement learning decision network based on hierarchical optimization. Under the hard real-time constraint of priority critical time-triggered TT flow, it optimizes the best-effort BE flow transmission efficiency, ultimately generating a gated control list for execution. This invention is primarily applied in design and manufacturing applications.

The invention discloses an electric power economic risk and customer value comprehensive evaluation system, which relates to the technical field of electric power system operation and comprises a multi-source data acquisition and preprocessing module, a multi-source data fusion module, a two-dimensional coupling evaluation modeling module, a dynamic weight adaptive adjustment module and an intelligent decision-making and visual output module. According to the method, the risk and the value are regarded as probability measurement, coupling is carried out by adopting optimal transmission methods such as Wasserstein gravity center and the like, and then quantiles, tail features and high-order moments are extracted from coupling distribution to serve as coupling evaluation vectors. The evaluation result of the distribution level fusion not only provides'single-point 'risk / value judgment, but also provides complete distribution form information. In combination with approximate Shapley-based interaction attribution, the system can reveal nonlinear interaction and marginal contribution between customer values and market / risk factors, thereby providing quantifiable and explainable decision basis for regionalized pricing, differentiated services, target customer retention strategies and risk slow release measures.

This invention belongs to the field of power system communication technology and discloses a method and related apparatus for optimizing power wireless local area network communication. The terminal acquires attribute reporting data and reports it to the wireless access point in the control time slot of the current superframe using a superframe-based dynamic time division multiple access mechanism. The wireless access point generates data time slot allocation information and physical layer parameter configuration information based on the attribute reporting data and sends it in the beacon frame of the next superframe. The terminal encodes data according to the configuration information and transmits it in the designated data time slot. The wireless access point receives the data. By jointly reporting service type, data volume, service quality level, and channel state information to the wireless access point, a complete decision-making basis is provided for cross-layer collaborative scheduling. This enables dynamic time slot scheduling of the media access control layer and adaptive configuration of carrier parameters and preamble sequences of the physical layer, effectively overcoming the problems of fixed resource allocation being unable to adapt to the dynamic needs of differentiated services and low resource utilization efficiency caused by independent optimization of two layers.

The application discloses a kind of 5G core network service function chain dynamic optimization method and system, it is related to mobile communication relevant technical field, the method includes: by three-dimensional perception module, real-time collection network service data;Service function chain optimization problem is decomposed into multi-stage decision model;Decomposed into long-term UPF deployment main problem, short-term traffic scheduling sub-problem, obtain global optimal solution by solving;Dynamically determine UPF deployment location and quantity;Dynamically select the traffic forwarding path that satisfies service level;In core layer and edge layer, construct double node deployment framework, and execute differentiated service function chain link control strategy.The technical problems that UPF deployment and traffic scheduling lack dynamic collaborative optimization in prior art, service function chain static optimization leads to low resource utilization, high energy consumption, poor service adaptability, to improve edge server resource utilization, reduce network energy consumption, optimize service quality and guarantee the technical effects of user experience.

This invention discloses a scenario-based forwarding method and system for multi-source seismic operational data, relating to the field of earthquake monitoring technology. The method includes: using an earthquake operational management platform, transforming multi-source seismic operational data into target information products for early warning terminals in different scenarios based on scenario attributes; constructing a network topology model including forwarding nodes and early warning terminals on the earthquake operational management platform, and collecting a real-time state parameter matrix of the current network links based on the network topology model; using the African Vulture optimization algorithm based on the real-time state parameter matrix to iteratively optimize the forwarding strategy of the target information products to obtain the optimal forwarding strategy; and using the earthquake operational management platform to send the target information products to the corresponding early warning terminals according to the optimal forwarding strategy and the scenario of the target information products. This invention solves the problems of difficulty in multi-source data fusion, lack of scenario-based differentiated services, and low network forwarding efficiency in existing technologies.

The invention discloses an SD-WAN traffic scheduling and collaborative management method for a multi-cloud environment, and the method comprises the steps: deploying an information collection agent in the multi-cloud environment, collecting the cloud node resource state and SD-WAN link transmission quality information in real time, enabling a constructed joint model to dynamically reflect the real-time state of a network, and breaking through the limitation of a conventional static model; the multi-objective optimization algorithm quickly recalculates different service flow optimal paths according to the update model, so that the flow is helped to avoid a resource shortage area, and the network flexibility and reliability are improved; traffic is classified carefully, a personalized optimization target is set, respective optimal paths are obtained through independent processing of an algorithm, differentiated service guarantee is provided for various services, and the service performance and the user experience are improved; the collaborative management node realizes effective collaboration of the cloud node and the network equipment, sends a scheduling instruction according to an optimal path, can quickly respond and update the instruction when a network state changes, realizes unified scheduling management, ensures dynamic optimization of flow, and improves network collaborative work and management efficiency.

A computer-implemented method for video data communication in a network includes receiving, by a node in the network, an Internet protocol (IP) data packet. The IP data packet includes an IP network layer header and an IP network layer payload. The IP network layer payload is parsed, by the node, to detect a Network Abstraction Layer Unit (NALU). The NALU includes a NALU header and a NALU payload. Priority information in the NALU header is detected by the node. The priority information indicates a transport priority of the NALU. A differentiated services (DS) field in the IP network layer header is updated based on the transport priority to generate an updated IP data packet. The updated IP data packet is forwarded to a second node in the network for discarding or processing based on the updated DS field.