Ultra-high-definition video network modal planning method and device based on network resource global perception

Abstract

The embodiment of the invention discloses an ultra-high-definition video network modal planning method and device based on network resource global perception. A specific embodiment of the method comprises the following steps: establishing an ultra-high-definition video distribution service demand and service quality representation model; the method comprises the following steps of: establishing an integrated representation theoretical model of computing, storing and forwarding resources in a multi-modal network element based on an ultra-high-definition video distribution service demand and a service quality representation model; and according to the integrated representation theoretical model, carrying out adaptive planning operation on the ultra-high-definition video network mode. According to the embodiment, the regularized generation template of the network modality can be formulated through global perception of service requirements and network resources, and the corresponding network modality is generated through dynamic compiling of a network program.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of ultra-high-definition video network modality planning, and specifically to an ultra-high-definition video network modality planning method and device based on global perception of network resources. Background Art

[0002] In recent years, the ultra-high-definition video industry has received significant attention from governments at all levels and received key support from national industrial policies. The country has successively introduced a number of policies to encourage its development. The Ministry of Industry and Information Technology and seven other government departments issued the "Guiding Opinions on Accelerating the High-Quality Development of the Audiovisual Electronics Industry," proposing the implementation of a 4K / 8K ultra-high-definition home access initiative. With this policy support, the era of widespread ultra-high-definition video is approaching. Simultaneously, with the advancement of digital, intelligent, and networked technologies, new network technologies such as SDN, P4, reconfigurable networks, and multimodal networks are empowering the network transmission and distribution of ultra-high-definition video. In particular, to address the diverse demands of current network services and applications and the fragmented nature of emerging network technologies, the innovative multimodal intelligent network concept aims to create a new paradigm for network development characterized by "full-dimensional definability, full-service transport, and end-to-end security." This concept also establishes a network innovation environment based on multimodal network equipment, providing a new approach to addressing the challenges of ultra-high-definition video distribution networks, including higher transmission efficiency, lower latency, and enhanced security.

[0003] Multimodal networks utilize a design principle that separates the technology system from the supporting environment. They build a rich, fully definable network baseline capability pool encompassing computing, transmission, storage, and interconnection resources. This allows various network technology systems to be dynamically loaded and operated within the multimodal network environment as network modalities. A multimodal network environment is a unified physical infrastructure that supports the coexistence of multiple or multiple network modalities. Through the network baseline capability pool and standardized hardware and software interfaces, it enables intelligent dynamic management and allocation of heterogeneous software and hardware resources, along with inherent security assurance. Network modalities are physical or virtual networks defined according to network technology systems, industry applications, and operational deployment. These networks are dynamically deployed within the multimodal network environment through standardized hardware and software interfaces. Processing is performed based on the customized hardware and software configurations, message formats, routing protocols, switching methods, forwarding logic, service characteristics, operational specifications, and security policies of each network modality. This enables the coexistence, independent evolution and transformation of multiple network modalities within the same network environment, as well as inter-modal isolation and inherent security. This provides precise, segmented, and customized support for various vertical industry applications. Summary of the Invention

[0004] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] Some embodiments of the present disclosure propose ultra-high-definition video network modality planning methods, devices, electronic devices and computer-readable media based on global perception of network resources to solve the technical problems mentioned in the above background technology section.

[0006] In the first aspect, some embodiments of the present disclosure provide an ultra-high-definition video network modality planning method based on global perception of network resources, the method comprising: establishing an ultra-high-definition video distribution business demand and service quality representation model; based on the ultra-high-definition video distribution business demand and service quality representation model, establishing an integrated representation theoretical model of computing, storage, and forwarding resources within a multi-modal network element; and performing adaptive planning operations on the ultra-high-definition video network modality based on the above-mentioned integrated representation theoretical model.

[0007] In the second aspect, some embodiments of the present disclosure provide an ultra-high-definition video network modality planning device based on global perception of network resources, the device including: a first establishment unit, configured to establish an ultra-high-definition video distribution business demand and service quality representation model; a second establishment unit, configured to establish an integrated representation theoretical model of computing, storage, and forwarding resources within a multi-modal network element based on the ultra-high-definition video distribution business demand and service quality representation model; a planning unit, configured to perform adaptive planning operations on the ultra-high-definition video network modality according to the above-mentioned integrated representation theoretical model.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0009] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0010] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the ultra-high-definition video network modality planning method based on global perception of network resources in some embodiments of the present disclosure, based on the massive heterogeneous programmable resources provided by multimodal network elements in the ultra-high-definition video distribution network, through global perception of business needs and network resources, a regularized generation template of the network modality is formulated, and the dynamic compilation of the network program is realized to generate the corresponding network modality. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0012] Figure 1 is a flowchart of some embodiments of the method for ultra-high-definition video network modality planning based on global perception of network resources according to the present disclosure; Figure 2 It is a quantitative mapping relationship diagram of services, modalities and resources in the ultra-high-definition video network modality planning method based on global perception of network resources in some embodiments of the present disclosure; Figure 3 This is a general framework diagram of an ultra-high-definition video network modality planning method based on global perception of network resources in some embodiments of the present disclosure; Figure 4 is a structural diagram of some embodiments of an ultra-high-definition video network modality planning device based on global perception of network resources according to the present disclosure; Figure 5 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0013] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0014] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0015] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0016] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0017] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0018] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0019] Figure 1 This is a process 100 of some embodiments of the method for planning ultra-high-definition video network modalities based on global perception of network resources in some embodiments of the present disclosure. The method for planning ultra-high-definition video network modalities based on global perception of network resources includes the following steps: Step 101: Establish an ultra-high-definition video distribution service demand and service quality representation model.

[0020] In some embodiments, an entity (e.g., a computing device) executing the method for planning an ultra-high-definition video network modality based on global network resource awareness can establish a model representing service requirements and quality of service for ultra-high-definition video distribution. This model includes the average bit rate and corresponding statistical characteristics of the service, as well as QoS requirements for bandwidth, packet loss, latency, and jitter.

[0021] For example, first, the demand modeling of ultra-high-definition video distribution services is divided into the average bit rate and dynamic characteristics of ultra-high-definition video distribution and video frame characteristics. The former (average bit rate and dynamic characteristics) includes bit rate modeling and statistical feature analysis, and the latter (video frame characteristics) includes frame type distribution and GOP structure. Different video frame types affect the video encoding format protocol; secondly, the QoS requirements (quality of service) of ultra-high-definition video distribution services need to be quantitatively analyzed around the four core indicators of bandwidth, packet loss, latency, and jitter. These indicators directly affect the smoothness, picture quality and user experience (QoE) of video transmission.

[0022] In practice, the above-mentioned execution entities can establish an ultra-high-definition video distribution service demand and service quality representation model through the following steps: The first step is to determine the average bit rate, dynamic characteristics and video frame characteristics of ultra-high-definition video distribution.

[0023] Average bitrate and dynamic characteristics include bitrate modeling and statistical feature analysis. ① Bitrate modeling considers both static and dynamic bitrate models. Static bitrate (CBR) is suitable for live broadcasts (such as sports events and live news broadcasts) and maintains a constant bitrate (e.g., 50-100 Mbps for 4K video). For example, 4K H.265 encoding uses a constant bitrate of 50-100 Mbps, while 8K AV1 encoding uses a constant bitrate of 150-300 Mbps. This approach offers the advantage of stable network bandwidth requirements, facilitating resource reservation. Dynamic bitrate (VBR) is commonly used in scenarios such as video on demand (e.g., streaming platforms) and cloud gaming. Its technical parameters dynamically adjust the bitrate based on content complexity. For example, low-complexity scenes (static images) use 20-50 Mbps (4K H.265); high-complexity scenes (explosion effects) use 100-200 Mbps (4K H.265). ② Statistical characteristics: Analyze peak bitrate to assess the network's burst traffic carrying capacity, which is divided into three levels: low carrying capacity (0-100Mbps); medium carrying capacity (100-200Mbps); and high carrying capacity (greater than 200Mbps). Analyze bitrate variance to measure bitrate volatility, which affects buffer design. The larger the variance, the larger the buffer design. Build a bitrate distribution model, modeling bitrate variation patterns based on Gaussian distribution or Poisson process.

[0024] Video frame characteristics include the distribution ratio of frame types and the GOP structure. ① Different video frame types influence the video encoding format protocol. Frame types typically include I-frames, P-frames, and B-frames. I-frames (key frames) contain complete image data and are independent of other frames. I-frames typically require more storage space because they don't share information. P-frames (predicted frames) contain only the difference information from the previous I-frame or P-frame. P-frames rely on previous reference frames and are therefore typically smaller than I-frames. B-frames (bidirectionally predicted frames) contain the difference information from the previous and next frames. B-frames can provide higher compression rates but require more processing power to decode. ② GOP (Group of Pictures) structure: GOP length is typically 1–5 seconds (e.g., 30–150 frames). Risks of long GOPs: The longer the GOP, the greater the range of error propagation (e.g., losing an I-frame can cause 5 seconds of video to be corrupted). Live broadcasts typically use short GOPs (e.g., 30 frames), while on-demand broadcasts can extend this to 120 frames.

[0025] The second step is to determine the service quality requirements for ultra-high-definition video distribution. The QoS requirements (quality of service requirements) for ultra-high-definition video distribution services require a quantitative analysis of four core metrics: bandwidth, packet loss, latency, and jitter. These metrics directly impact the smoothness, image quality, and user experience (QoE) of video transmission.

[0026] ① Customized bandwidth requirements must meet both static resolution requirements and dynamic bitrate fluctuations. A flexible resource allocation strategy should be designed with a safety margin. Bandwidth requirements are customized; a flexible resource allocation strategy should be designed with a safety margin. Specifically, low bandwidth (less than 200 Mbps) is allocated for low-security scenarios (video failure rate 5-10%), and high bandwidth (greater than 200 Mbps) is allocated for high-security scenarios (video failure rate 0-5%). The video failure rate is calculated as the ratio of unplayable video duration to total video duration.

[0027] Packet loss tolerance measures the sensitivity of ultra-high-definition video to packet loss. This requires distinguishing between real-time and non-real-time scenarios, and using a combination of FEC, retransmission, and priority protection to mitigate the impact of packet loss. Packet loss tolerance is calculated by dividing the post-packet loss video quality score (peak signal-to-noise ratio (PSNR)) by the original video quality score (peak signal-to-noise ratio (PSNR)).

[0028] ③ Latency should be capped end-to-end by scenario. Encoding latency: 4K video H.265 encoding is approximately 20–50ms (strongly dependent on hardware performance). Transmission latency: This includes propagation latency (approximately 5ms / 1000km for optical fiber) and queuing latency (which increases significantly during network congestion). Decoding and buffering latency: Decoding is approximately 10–30ms, with initial buffering typically designed for 2–5 seconds (for on-demand content).

[0029] ④ Jitter is defined as the variance of the time interval between packet arrivals (over a 12-hour historical period). Setting a buffer can effectively mitigate jitter. Presetting a buffer (e.g., 200ms) is simple but may introduce additional latency.

[0030] The third step is to establish an ultra-high-definition video distribution service requirement and service quality representation model based on the average bit rate and dynamic characteristics, video frame characteristics, and the service quality requirement information. Specifically, the average bit rate and dynamic characteristics, video frame characteristics, and the service quality requirement information can be mapped into the ultra-high-definition video distribution service requirement and service quality representation model. For example, the average bit rate and dynamic characteristics, video frame characteristics, and the service quality requirement information can be mapped into the ultra-high-definition video distribution service requirement and service quality representation model in the form of a table or knowledge graph.

[0031] Step 102: Based on the ultra-high-definition video distribution service requirements and service quality representation model, an integrated representation theoretical model of computing, storage, and forwarding resources within a multimodal network element is established.

[0032] In some embodiments, the above-mentioned execution subject can establish an integrated representation theoretical model of computing, storage, and forwarding resources within a multimodal network element based on the ultra-high-definition video distribution service demand and service quality representation model. Based on the ultra-high-definition video distribution service demand and service quality representation model, an integrated representation theoretical model of computing, storage, and forwarding resources within a multimodal network element can be established. Figure 2 As shown, a quantitative mapping relationship between business, modality and resources is constructed, and a quantitative mapping between "business-modality" and "modality-resources" is proposed.

[0033] In practice, the above-mentioned execution entities can establish an integrated representation theoretical model of computing, storage, and forwarding resources within multimodal network elements through the following steps: In the first step, a business modality mapping relationship between the business dimension and the modality dimension is constructed for the first modality relationship. The first modality relationship may represent the relationship between the business dimension and the modality dimension.

[0034] Regarding the first modal relationship, we need to consider the clustering of diverse businesses and analyze the mapping between business clusters and modalities. Businesses can be characterized from different dimensions, and the service capabilities of modalities can also be demonstrated from corresponding dimensions. The quantitative goal is to decompose businesses into modalities, that is, assigning a modal to each business so that the business falls within the service scope of the modal or is as close as possible.

[0035] Business dimensions may include: live video, video on demand, video playback, and video caching.

[0036] Modal dimensions may include: addressing, forwarding, control, and security.

[0037] In the second step, for the second modal relationship, a modal resource mapping relationship between the modal dimension and the resource dimension is constructed. The second modal relationship can be a mapping relationship between the modal dimension and the resource dimension.

[0038] Regarding the "modality-resource" approach, the focus is on the quantitative relationship between the service clusters adapted by the modality and the baseline capabilities (forwarding resources, computing resources, and storage resources) formed by the collaborative resources. Using baseline functional capabilities as a link, the quantitative mapping relationship between modalities and resources is studied. Notably, this solution introduces the concept of baseline capabilities. Baseline capabilities are fundamental network functions such as forwarding, control, addressing, and security. Baseline capabilities must be implemented based on resources, for example, routing requires multiple point-to-point links. Resource dimensions can include forwarding resources, computing resources, and storage resources.

[0039] The third step is to establish an integrated representation theoretical model for computing, storage, and forwarding resources within multimodal network elements based on the aforementioned service modality mapping relationships and modality resource mapping relationships. This creates a network modality regularization generation template that comprehensively represents service requirements and network resource representations. By combining the computing, storage, and forwarding resources of multimodal network element devices, a dynamic programming algorithm is used to accurately characterize network modality programs from the dimensions of addressing, forwarding, control, and security, clarifying the hierarchical mapping relationships between "service-modality" and "modality-resource." In other words, the service modality mapping relationship and the modality resource mapping relationship can be mapped into an integrated representation theoretical model.

[0040] Step 103 : performing adaptive planning operations on the ultra-high-definition video network modality according to the above-mentioned integrated representation theoretical model.

[0041] In some embodiments, the aforementioned execution entities can perform adaptive planning operations for ultra-high-definition video network modalities based on the aforementioned integrated representation theoretical model. Specifically, based on the mapping relationships in the aforementioned integrated representation theoretical model and in combination with the computing, storage, and forwarding resources of multimodal network element devices, network modality programs can be accurately characterized from the perspectives of addressing, forwarding, control, and security. This can clarify the hierarchical mapping relationships between "service-modality" and "modality-resource," thereby enabling adaptive planning for ultra-high-definition video network modalities (including ultra-high-definition video protocols such as DASH, SRT, HLS, RTP, and RTMP).

[0042] Further references Figure 3 ,exist Figure 3 In the overall framework, the left and right sides are the sending and receiving ends of ultra-high-definition video, and the middle part is a multimodal network environment composed of multimodal network element devices. Each multimodal network element runs multiple network modes on demand to support multiple ultra-high-definition video distribution protocols, including DASH (dynamic adaptive streaming media transmission), SMT (synchronous multicast transmission), HLS (HTTP live streaming transmission), etc., to ensure that video content can be efficiently transmitted in multiple ways. Therefore, the present invention mainly carries out ultra-high-definition video network mode planning through global perception of network resources for ultra-high-definition video business needs and multimodal network environments, so as to flexibly select the optimal transmission method according to different application scenarios and user needs, and further improve the efficiency of ultra-high-definition video distribution and user experience.

[0043] Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an ultra-high-definition video network modality planning device based on global perception of network resources. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the ultra-high-definition video network modality planning device based on global perception of network resources can be specifically applied to various electronic devices.

[0044] like Figure 4 As shown, in some embodiments, an apparatus 400 for planning ultra-high-definition video network modalities based on global network resource awareness includes: a first establishing unit 401, a second establishing unit 402, and a planning unit 403. The first establishing unit 401 is configured to establish a model representing service requirements and quality of service for ultra-high-definition video distribution; the second establishing unit 402 is configured to establish an integrated theoretical model representing computing, storage, and forwarding resources within a multi-modal network element based on the model; and the planning unit 403 is configured to perform adaptive planning operations on ultra-high-definition video network modalities based on the integrated theoretical model.

[0045] It is understandable that the units recorded in the ultra-high-definition video network mode planning device 400 based on global perception of network resources are the same as those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the ultra-high-definition video network modality planning device 400 based on global perception of network resources and the units contained therein, and will not be repeated here.

[0046] Reference below Figure 5 , which shows a schematic structural diagram of an electronic device (such as a computing device) suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure. Figure 5 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions, which, when executed, enable the processor to execute any ultra-high-definition video network modal planning method based on global perception of network resources. The processor is used to provide computing and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium, which, when executed by the processor, enables the processor to execute any ultra-high-definition video network modal planning method based on global perception of network resources. The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present disclosure, and does not constitute a limitation on the computer device to which the solution of the present disclosure is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0047] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0048] In one embodiment, the processor is used to run a computer program stored in a memory to implement the following steps: establishing an ultra-high-definition video distribution service demand and service quality representation model; based on the ultra-high-definition video distribution service demand and service quality representation model, establishing an integrated representation theoretical model of computing, storage, and forwarding resources within a multimodal network element; and performing adaptive planning operations on the ultra-high-definition video network modality based on the above-mentioned integrated representation theoretical model.

[0049] An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the ultra-high-definition video network modality planning method based on global perception of network resources disclosed in the present disclosure.

[0050] The computer-readable storage medium may be an internal storage unit of the computer device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., provided on the computer device.

[0051] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0052] The above descriptions are merely some preferred embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for ultra-high-definition video network mode planning based on global perception of network resources, characterized in that: include: Establish a model to represent the service requirements and quality of service of ultra-high-definition video distribution; Based on the UHD video distribution service requirements and service quality representation model, an integrated representation theoretical model for computing, storage, and forwarding resources within multimodal network elements is established; According to the integrated representation theoretical model, adaptive planning operations are performed on the ultra-high-definition video network modality.

2. The method according to claim 1, characterized in that The establishment of an ultra-high-definition video distribution service demand and service quality representation model includes: Determine the average bit rate and dynamic and video frame characteristics of ultra-high-definition video distribution; Determine quality of service requirements for ultra-high-definition video delivery; An ultra-high-definition video distribution service requirement and service quality representation model is established based on the average bit rate and dynamic characteristics, video frame characteristics, and the service quality requirement information.

3. The method according to claim 2, characterized in that The above-mentioned integrated representation theoretical model of computing, storage, and forwarding resources within multimodal network elements is established based on the ultra-high-definition video distribution service requirements and service quality representation model, including: For the first modal relationship, a business modality mapping relationship between the business dimension and the modality dimension is constructed; For the second modal relationship, a modal resource mapping relationship between the modal dimension and the resource dimension is constructed; According to the service modality mapping relationship and the modality resource mapping relationship, an integrated representation theoretical model of computing, storage, and forwarding resources within a multimodal network element is established.

4. An ultra-high-definition video network modality planning device based on global perception of network resources, characterized in that: include: A first establishing unit is configured to establish an ultra-high-definition video distribution service demand and service quality representation model; The second establishing unit is configured to establish an integrated representation theoretical model of computing, storage, and forwarding resources within a multimodal network element based on the ultra-high-definition video distribution service requirements and service quality representation model; The planning unit is configured to perform adaptive planning operations on the ultra-high-definition video network modality according to the integrated representation theoretical model.

5. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

6. A computer-readable medium, characterized in that A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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