A content caching method based on multi-network channel transmission

Abstract

The present application relates to a kind of content caching method based on multi-network channel transmission, belong to wireless communication technical field.The method includes according to the characteristics of scalable video coding, video content is encoded into base layer and enhancement layer;The video content of base layer is stored in macro base station, the video content of enhancement layer is stored in small base station, and the system architecture of macro base station and small base station small base station layered caching is constructed;According to the content popularity of video content, the request probability of different encoding layers of user from macro base station or / and small base station to obtain video content is calculated;According to caching strategy and user quality demand, the minimum delay satisfaction model is constructed;Delay satisfaction model is converted, and optimal caching strategy is obtained using alternating iterative algorithm.The present application can effectively improve user delay satisfaction, and have broad application prospect.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology and relates to a content caching method based on multi-network channel transmission. Background Technology

[0002] With the booming development of multimedia applications, real-time entertainment consisting of video and audio has become a major user traffic request. The astonishing growth of mobile video data has become a significant obstacle to improving user experience quality. Utilizing heterogeneous cellular networks with edge caching assistance, popular video data can be deployed within cells and smaller cells, allowing users to complete content requests through multiple network channels. This includes direct acquisition from cells, cellular relays facilitating content acquisition from smaller cells, and collaborative content sharing between smaller cells. Distributing requested content to users through multiple network channels provides various request paths, allowing users to prioritize the path with the shortest latency, significantly reducing request latency. However, the limited storage space of edge nodes cannot handle the massive amount of video content caching. Furthermore, users have different quality requirements for different videos. For example, mobile users only require low-definition smooth playback for sports games and news reports, while demanding high-definition viewing quality for entertainment programs and movies. This necessitates edge nodes caching various bitrate versions of videos, introducing significant redundancy into caching deployment.

[0003] To utilize edge storage space more efficiently, Scalable Video Coding (SVC) technology can be used to encode video in layers, leveraging its unique layering feature. SVC can flexibly and conveniently generate video layers with different bitrates. Considering the impact of user channel conditions on transmission, only the base layer bitstream is transmitted and decoded when channel conditions are poor; conversely, when channel conditions are good, the enhancement layer bitstream is transmitted and decoded to improve video quality. By considering video popularity, a base layer video caching strategy is determined to adapt to diverse user needs and channel conditions. Simultaneously, enhancement layer videos with different frame rates and bitrates are stored at edge nodes according to user requirements, avoiding the large capacity consumption of high-quality videos and the redundant storage of multiple video versions. This allows for flexible transmission of video layers to users, providing more personalized content services. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a content caching method based on multi-network channel transmission. Addressing the diversity of video quality requests from users and the limited caching space at edge nodes, a cooperative caching latency satisfaction minimization model based on SVC is designed in a heterogeneous cellular network scenario. Considering the effectiveness of storage space, storage scheme optimization is further incorporated into the caching model. Basic layer video content is placed in macro base stations with global request capabilities for users to watch basic videos; enhancement layers are placed in local small base station areas according to user needs and channel conditions to provide personalized content for users. Next, the Dinkelbach algorithm is used to transform the latency satisfaction minimization problem, obtaining the optimal latency satisfaction. Users can choose the path with the shortest transmission latency to obtain the requested content, reducing transmission latency while ensuring the quality of the requested video. Finally, a joint optimization strategy for quality requirements and caching is designed using dynamic programming and bilateral switching.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A content caching method based on multi-network channel transmission, the method specifically includes the following steps:

[0007] S1: Based on the characteristics of scalable video coding, the video content is encoded into the base layer and the enhancement layer;

[0008] S2: Store the video content of the basic layer in macro base stations and the video content of the enhancement layer in small base stations, and build a system architecture with hierarchical caching of macro base stations and small base stations;

[0009] S3: Calculate the probability of a user requesting different encoding layers of video content from macro base stations and / or small base stations based on the popularity of the video content.

[0010] S4: Based on user request latency and satisfaction under different transmission paths, construct a latency-satisfaction minimization model to optimize caching strategies and user quality requirements;

[0011] S5: Transform the latency satisfaction model and use an alternating iterative algorithm to obtain the optimal caching strategy.

[0012] The beneficial effects of this invention are as follows:

[0013] This invention addresses the coverage characteristics of small and macro base stations and the hierarchical coding characteristics of SVC (Single-Video Capability). A novel caching scheme is designed, storing basic viewing-capable video in macro base stations for global user access, while enhancing video quality is stored in small base stations for local user requests. This ensures users can access requested content from the shortest latency network channel, while maximizing cache space efficiency to meet users' varying video quality needs. Furthermore, in an edge node collaboration scenario, a request probability and latency model is constructed. Based on this, a latency satisfaction model under user quality requirements is derived. The Dinkelbach algorithm is used to remodel the latency satisfaction problem, yielding optimal latency satisfaction. Finally, under the optimal latency satisfaction solution, a joint optimization algorithm for quality requirements and caching is designed. The problem is decomposed into user quality requirement sub-problems and cache placement sub-problems. Dynamic programming is used to solve for user quality requirements, and a caching strategy maximizing latency satisfaction is obtained through a priority list combined with bilateral exchange.

[0014] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0016] Figure 1 This is a flowchart of a content caching method based on multi-network channel transmission according to an embodiment of the present invention;

[0017] Figure 2 This is a network system architecture diagram according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0020] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0021] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0022] Please see Figure 1 , Figure 1 This is a flowchart of a content caching method based on multi-network channel transmission according to an embodiment of the present invention; as follows: Figure 1 As shown, this invention provides a content caching method based on multi-network channel transmission, specifically including the following steps:

[0023] Step S1: Based on the characteristics of scalable video coding, encode the video content into the base layer and the enhancement layer;

[0024] Step 1.1: For the content I = {1,2,...,I} in the video library, using the coding characteristics of SVC, each video content can be encoded into l layers, including a base layer and l-1 enhancement layers. The base layer ensures the most basic viewing experience of the video, while the enhancement layers contain supplementary information of the basic video content, which is provided to the user when the channel environment is good or the channel resources are abundant, in order to improve the quality of the received video. The more enhancement layers there are, the higher the quality level of the video. Encoding different video layers according to different needs can improve the coding quality.

[0025] Please see Figure 2 , Figure 2 This is a network system architecture diagram according to an embodiment of the present invention; as shown below. Figure 2 As shown, in conjunction with the network system architecture of this invention, the invention is constructed in the following manner:

[0026] S2: Store the video content of the basic layer in macro base stations and the video content of the enhancement layer in small base stations, and build a system architecture with hierarchical caching of macro base stations and small base stations;

[0027] Step 2.1: Since macro base stations have a wider coverage area and can serve more users, the SVC-encoded base layer video content is stored in the macro base station during the caching stage. Binary variables are defined. For macro base station cache metrics, c i,0 =1 indicates that the base layer cache of video content i is in the macro base station, c i,0 =0 indicates that it is not cached.

[0028] Step 2.2: Users within different small cell coverage areas have different preferences for different video quality levels. Therefore, enhancement layers for different videos are cached in the small cells according to user requests. Define binary variables. Let c be the layer cache index for small base station n. i,n,l =1, then the l-th layer of the i-th video content is stored in the small base station n, c i,n,l =0 indicates that it is not cached.

[0029] S3: Calculate the probability of a user requesting video content from different coding layers from macro base stations and / or small base stations based on the popularity of the video content.

[0030] Step 3.1: Sort the video content in descending order of popularity. Content with higher popularity has a smaller index. Model the content popularity as a Zipf distribution, then the request probability of the i-th content can be expressed as:

[0031]

[0032] Here, κ represents the popularity skewness. The larger the κ value, the more uneven the distribution of content popularity, meaning that less content is needed to satisfy most users' requests.

[0033] Step 3.2: For the request probabilities of the base layer and the enhancement layer, since there are multiple layers of content, it is necessary to give the request probability of each layer.

[0034] Step 3.3: Based on the request probability of the encoding layer of each video content, calculate the probability of the user obtaining the base layer of video content from the macro base station and the probability of obtaining the enhancement layer of video content from the local small base station and the cooperative small base station.

[0035] Model the standard definition video (i.e., the base layer) of the i-th video content as follows: The probability of a high-definition video (i.e., enhancement layer) request can then be modeled as g HDV (i)=1-g SDV (i). Assuming the probability of a user requesting each enhancement layer is the same, the probability of a user requesting the l-th layer of the i-th content is obtained as follows:

[0036]

[0037] The probability that user u obtains video content i at the l-th layer from local small base station n is:

[0038]

[0039] Among them, c i,n,l This indicates whether the l-th layer of content i is cached in the small base station n.

[0040] Similarly, the probability of user u obtaining content i from the macro base station in the basic layer is:

[0041]

[0042] Among them, c i,0 Indicates whether the base layer of content i is stored in the macro base station.

[0043] When a user cannot obtain the requested enhancement layer from a small base station, the local small base station will search from other small base stations. Therefore, the probability that user u obtains the l-th layer of video content i from the cooperating small base station m can be calculated as follows:

[0044]

[0045] Among them, M n,m This represents the set of cooperating small base stations m around small base station n that meet the transmission conditions, and the channel condition log(1+SNR) between small base station m and macro base station m. m,0 When )>E, m∈M n,m ; The enhancement layer l is used to determine whether the cooperating small base station m (n≠m) of small base station n has cached content i. If the value is in the small base station m, it means that the enhancement layer l of content i is cached; otherwise, it is not cached.

[0046] S4: Based on the caching strategy and user quality requirements, construct a model that minimizes latency satisfaction;

[0047] Step 4.1: Calculate the data rate from the local small cell base station to the user, and the latency of the enhancement layer for the user to download video content from the local small cell base station;

[0048] When a user retrieves requested content from a local small cell base station, the requested content can be transmitted via a wireless link. The data rate from small cell base station n to user u is:

[0049]

[0050] Among them, B SBS g is the channel bandwidth from the small base station to the user. n,u It is the channel gain between small base station n and user u, g n,u Depend on Given, where h n,u It is a random variable that follows a Gaussian distribution (0,1). It is the path loss constant, d n,u P is the distance between small base station n and user u, where ν is the path loss exponent; SBS This refers to the transmission power of the small base station; It is co-channel interference from other small base stations; σ 2 It is the power of additive white Gaussian noise.

[0051] The latency of user u downloading content i from local small base station n at layer l can be obtained as follows:

[0052]

[0053] Among them, s i,u,l It is the size of the l-th layer of the video content i requested by user u.

[0054] Step 4.2: Calculate the downlink transmission rate from the macro base station to the local small base station, and the latency of the base layer for the user to download video content from the macro base station;

[0055] The downlink transmission rate from the macro base station to the small base station n is:

[0056]

[0057] Among them, B MBS g is the channel bandwidth from macro base station to small base station. 0,n It is the channel gain of the macro base station; P MBS This is the maximum transmit power of the macro base station.

[0058] The base layer latency for user u to download content i from the macro base station can be obtained as:

[0059]

[0060] Among them, s i,u,1It is the size of the base layer of the video content i requested by user u.

[0061] Step 4.3: Calculate the uplink data rate from the cooperative small base station to the macro base station, and the latency of the enhancement layer for the user to download video content from the cooperative small base station;

[0062] The uplink data rate from the cooperative small base station m to the macro base station is:

[0063]

[0064] The latency of user u downloading video content i from the cooperating small base station m at layer l is:

[0065]

[0066] in, V is used to indicate whether the cooperative small base station m (n≠m) caches the enhancement layer l of content i; V is a large constant that makes small base stations that do not contain the requested content i ignored; s i,u,l It is the size of the enhancement layer l for the video content i requested by user u.

[0067] Step 4.4: Based on the rates and delays obtained in Steps 4.1-4.3, calculate the total delay of all video coding layers requested by the user;

[0068] Construct a binary variable for user quality requirements based on the number of layers. k u,i,l =1 indicates that user u requests layer l of video content i; otherwise, it indicates that no request is made. Therefore, using This represents the total number of layers obtained by user u when requesting content i. Therefore, the total latency for all user requests across all layers within the system can be obtained.

[0069]

[0070] Step 4.5: Calculate the success probability of the enhancement layer for the user to download video content from the cooperating small base station based on the data rate at which the user downloads the video content from the cooperating small base station;

[0071] Choosing the satisfaction level of successfully acquired video as a performance metric, user satisfaction with video quality can be quantified as the product of the user's required number of layers and whether that layer was successfully transmitted. The data rate r from the user downloading content i from small base station n... n,u The probability of successful transmission of content i downloaded by the user from small base station n at layer l is:

[0072]

[0073] Where, σ 2For transmit power and normalized noise variance; ρ i,l This represents the transmission rate threshold of layer l for successfully transmitting video content i.

[0074] Step 4.6: Calculate the user's satisfaction with the requested video content based on the user's video quality preferences;

[0075] Different users have different preferences for video quality. To calculate a user's satisfaction with the requested video based on their video quality preferences, satisfaction is obtained when the requested video layer is successfully transmitted. Therefore, the satisfaction obtained by user u after requesting layer l of content i is:

[0076] Sat u,i,l =c i,0 k u,i,l P{R i,l ≥ρ i,l}

[0077] Among them, c i,0 For whether the macro base station caches content i, it is a binary variable of the underlying layer; k u,i,l This is a binary variable representing layer l that indicates whether user u requests content i.

[0078] Step 4.7: Determine the user's satisfaction function per unit latency based on the ratio of the user's satisfaction with the requested video content to the total latency of all video encoding layers requested by the user.

[0079] Therefore, we define latency satisfaction to construct a user satisfaction function for a unit latency:

[0080]

[0081] Step 4.8: Based on the user's satisfaction function for unit latency and the constraints, construct a latency-minimizing satisfaction model.

[0082] To solve the caching strategy with optimal latency satisfaction, the cache placement problem that maximizes latency satisfaction is modeled as follows:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] Wherein, the optimization variable c is the cache placement variable for macro base stations and small base stations n; k is the user request layer requirement variable; C1 and C2 represent the constraints of the binary cache variable; C3 is the constraint of the binary variable of the user request layer; C4 is the cache capacity constraint of the macro base station; C5 is the cache capacity constraint of the small base station; and C6 indicates that the number of requests for video content i cannot exceed the layer of video content i.

[0091] S5: Transform the latency satisfaction model and use an alternating iterative algorithm to obtain the optimal caching strategy.

[0092] Step 5.1: Considering the fractional form in the objective function, transform P1 using the Dinkelbach method and define a new parameter. To express the satisfaction with the time delay, its fractional expression is given:

[0093]

[0094] Where X(w) is the cache placement With the number of user needs The set of variables, after transformation, will not introduce additional constraints to the objective function.

[0095] Assumption To achieve optimal time delay satisfaction, Theorem 1 is given for solving the joint variable X(w).

[0096] Theorem 1: The optimal solution X can be obtained if and only if the following equation is satisfied. * (w).

[0097]

[0098] Proof: Fixed When the variable X(w) is constant, the function becomes B-Ax, where each set of X(w) corresponds to a straight line with a slope of 0. Since the y-intercept Sat(X(w)) is not positive, it is also non-negative. Therefore, When the optimal solution is to the right of the current solution, X(w) <X * (w), When the optimal solution is to the left of the current solution, X(w) > X. * (w), we can know When, the optimal solution X(w) = X * (w).

[0099] Step 5.2: According to Theorem 1, the time delay satisfaction model P1 can be transformed into a parametric programming problem P2:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] It can be known for Theorem 2 is given regarding the monotonicity and monotonicity of the molecule.

[0108] Theorem 2: for It is a monotonically decreasing function.

[0109] Proof: For any and The optimal solutions to the corresponding problem P2 are X'(w) and X(w), respectively, which can be obtained as follows:

[0110]

[0111] It can be seen that, for It is a monotonically decreasing function.

[0112] In the time-delay satisfaction maximization algorithm, for a given time delay, in each iteration... Solve problem P2 to obtain a feasible solution for the corresponding content layer strategy X(w), and update the latency satisfaction based on X(w). Until satisfied Optimal latency satisfaction can be obtained.

[0113] Step 5.3: For the transformed problem P2, the joint optimization problem of user requirement strategy and caching strategy cannot be solved directly. Therefore, to reduce the complexity of the problem, a Joint Optimization of Quality Requirements and Cache (JQRC) algorithm is designed. This algorithm decomposes the problem into two subproblems: the user quality requirement subproblem and the cache placement subproblem. Given the cache placement, the user quality requirement is solved. Based on the optimization results of the user quality requirement subproblem, the cache placement subproblem is introduced to optimize latency satisfaction.

[0114] Step 5.4: Regarding the user quality requirements, given the known caching strategy, allocate an appropriate number of video layers to users with different download speeds, i.e., decide how many layers to deliver to each user, thereby maximizing the satisfaction of all users. Let δ be the request level of user u to content i. u,i The relationship between the size of the maximum number of layers l that can be provided can be used to obtain the user quality requirement strategy k:

[0115]

[0116] Where l = 1, 2, ..., L.

[0117] Therefore, by determining δ u,i Complete the deployment of user quality requirement strategy k. Simultaneously, define a rate threshold as the basis for the user's selection of the number of layers. Discretize this threshold into multiple rate intervals, providing different video layers to the user in different rate intervals. That is, when user u requests video content i, if the user's rate r... n,u exist In the interval, the small base station provides the user with the l-th layer of video content i; where, This indicates the transmission rate of the l-th layer of content requested by user u in video content i. This represents the transmission rate of the (l+1)th layer of content requested by user u in video content i.

[0118] The choice of the number of layers within each rate interval can be solved using dynamic programming. For each rate interval, define... For a data transmission rate within the range of rε≤R<ε(r+1), the latency satisfaction achievable for the highest-level content of requested video content i at layer l can be expressed as follows:

[0119]

[0120] In each recursion, we gain arrive The maximum value; by finding the maximum This allows us to determine the maximum video layer corresponding to the current rate range as d.

[0121] Step 5.5: Under the user quality requirement strategy, one enhancement layer content can be cached in multiple small base stations, and multiple enhancement layer contents can also be cached in one small base station. When the enhancement layer content cached by a small base station changes, the content cached by the cooperating small base stations will also be affected. Therefore, many-to-many matching based on matching theory can be used to complete the cache placement, solving the problem that the result of one matching pair will be affected by other matching pairs. For the disjoint sets of small base stations n and enhancement layer contents l in the priority list, defined as disjoint non-empty sets N and L respectively, the many-to-many matching problem is that in sets N and L, at least one element n in N can be matched with an element l in L.

[0122] Defined as a commutative matching function, where l∈Φ(n), l′∈Φ(n′),

[0123] In the formula, Φ\{(n,l),(n′,l′)} represents the pairing of small base station n and enhancement layer content l without removing the pairing of small base station n' and enhancement layer content l'; Φ(n)\{l} represents the pairing of small base station n and enhancement layer content l without removing the pairing of small base station n; Φ(n′)\{l′} represents the pairing of small base station n' and enhancement layer content l' without removing the pairing of small base station n'; Φ(n) represents the set of enhancement layer content of small base station n, and Φ(n′) represents the set of enhancement layer content of small base station n'.

[0124] Given a pair (l, l′), when (l, l′) satisfies When a swapping pair is successfully matched, the two small base stations can increase the overall latency satisfaction by swapping during the selection of buffered content, until there are no more swapping pairs in the system, at which point the matching reaches a stable state.

[0125] The establishment of the priority list for enhancement layers for small base stations is based on the latency satisfaction of a certain enhancement layer cached in small base station n, which can be expressed as:

[0126]

[0127] The priority list of enhancement layer content for small base stations is established based on the latency satisfaction of enhancement layer l in a certain small base station, which can be expressed as:

[0128]

[0129] First, establish the initial matching state between the small base station and the enhancement layer, based on β. n and β iPriority lists are established for small base stations and enhancement layers respectively. Each enhancement layer sends a request to the small base station with the highest priority in the priority list. Each small base station accepts the enhancement layer with the highest priority in the priority list according to the priority list, until the small base station's buffer space is completely used up or the unmatched enhancement layer is rejected by all small base stations.

[0130] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include ROM, RAM, disk, or optical disk, etc.

[0131] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A content caching method based on multi-network channel transmission, characterized in that: The method specifically includes the following steps: S1: Based on the characteristics of scalable video coding, the video content is encoded into the base layer and the enhancement layer; S2: Store the video content of the basic layer in macro base stations and the video content of the enhancement layer in small base stations, and build a system architecture with hierarchical caching of macro base stations and small base stations; S3: Calculate the probability of a user requesting different encoding layers of video content from macro base stations and / or small base stations based on the popularity of the video content. S4: Based on the caching strategy and user quality requirements, construct a latency satisfaction model that minimizes latency. The specific process includes: S41: Calculate the data rate from the local small cell to the user, and the latency of the enhancement layer for the user to download video content from the local small cell; S42: Calculate the downlink transmission rate from the macro base station to the local small base station, and the latency of the base layer for the user to download video content from the macro base station; S43: Calculate the uplink data rate from the cooperative small base station to the macro base station, and the latency of the enhancement layer for the user to download video content from the cooperative small base station; S44: Based on the rates and delays of S41-S43, calculate the total delay of all video coding layers requested by the user; S45: Calculate the success probability of the enhancement layer when the user downloads video content from the cooperating small base station based on the data rate at which the user downloads video content from the cooperating small base station. S46: Calculate the user's satisfaction with the requested video content based on the user's video quality preferences; S47: Determine the user's satisfaction function per unit latency based on the ratio of the user's satisfaction with the requested video content to the total latency of all video encoding layers requested by the user. S48: Based on the user's satisfaction function for unit latency and the constraints, a latency-minimizing satisfaction model is constructed. The latency-minimizing satisfaction model specifically includes: ， in, This represents a user satisfaction function based on a unit latency. Represents the total latency of user requests across all levels within the system; optimization variable For macro base stations and local small base stations The cache stores variables; The user's request layer number requirement is a variable. Represents a set of small base stations. This represents the collection of video content in the video library. This represents the set of coding layers for scalable video coding. Indicates whether the macro base station caches video content. binary variables in the base layer Indicates video content The Is the layer cached in the local small base station? middle, Indicates user Request content? layer binary variables, Indicates user Request video content Size of the base layer Indicates user Request video content reinforcement layer Size, This indicates the maximum buffer capacity of the macro base station. Indicates local small base station Maximum cache capacity C1 and C2 represent the number of coding layers in scalable video coding; C3 represents the constraint on the binary buffer variable; C4 represents the constraint on the user-requested layer binary variable; C5 represents the buffer capacity constraint of the macro base station; C6 represents the buffer capacity constraint of the SBS; C6 represents the constraint on the video content. The number of requests cannot exceed the video content. The number of floors; S5: Transform the latency satisfaction model and use an alternating iterative algorithm to obtain the optimal caching strategy.

2. The content caching method based on multi-network channel transmission according to claim 1, characterized in that: S2 specifically includes the following steps: S21: During the caching phase, the macro base station stores the base layer video content after scalable video encoding in the macro base station and defines binary variables. For macro base station cache metrics, Indicates video content The base layer cache is located in the macro base station. Indicates video content The base layer is not cached in the macro base station; S22: Cache different video enhancement layers in the small base station according to user requests; define binary variables. For small base stations The layer cache metrics, if Then the first The first video content Layer storage in small base stations middle, This indicates that the cache is not cached.

3. The content caching method based on multi-network channel transmission according to claim 1, characterized in that: S3 specifically includes the following steps: S31: Sort video content in descending order of popularity, with higher-popularity content having smaller indexes. Model the content popularity as a Zipf distribution and calculate the request probability of each video content. S32: Calculate the request probability of the encoding layer for each video content based on the request probability of each video content. S33: Based on the request probability of the encoding layer of each video content, calculate the probability of the user obtaining the base layer of video content from the macro base station and the probability of obtaining the enhancement layer of video content from the local small base station and the cooperative small base station.

4. The content caching method based on multi-network channel transmission according to claim 1, characterized in that: The transformation of the latency satisfaction model in S5 specifically includes: The Dinkelbach method is used to transform the delay satisfaction model P1, and a new parameter is defined The delay satisfaction is represented, and its fractional expression is given: ， wherein, for cache placement with user demand layer a set of variables; The time delay satisfaction model P1 is transformed into a parametric programming problem P2: ， in, This represents a user satisfaction function based on a unit latency. This is represented as the optimal latency satisfaction. Represents the total latency of user requests across all levels within the system; optimization variable For macro base stations and local small base stations The cache stores variables; The user's request layer number requirement is a variable. Represents a set of small base stations. This represents the collection of video content in the video library. This represents the set of coding layers for scalable video coding. Indicates whether the macro base station caches video content. binary variables in the base layer Indicates video content The Is the layer cached in the local small base station? middle, Indicates user Request content? layer binary variables, Indicates user Request video content Size of the base layer Indicates user Request video content reinforcement layer Size, This indicates the maximum buffer capacity of the macro base station. Indicates local small base station Maximum cache capacity C1 and C2 represent the number of coding layers in scalable video coding; C3 represents the constraint on the binary buffer variable; C4 represents the constraint on the user-requested layer binary variable; C5 represents the buffer capacity constraint of the macro base station; C6 represents the constraint on the buffer capacity of the small base station; C6 represents the constraint on the video content. The number of requests cannot exceed the video content. The number of floors.

5. The content caching method based on multi-network channel transmission according to claim 4, characterized in that: The S5 method for obtaining the optimal caching strategy using the alternating iterative algorithm specifically includes the following steps: S51: The parametric programming problem P2 is decomposed into a user quality requirement subproblem and a cache placement subproblem using a joint optimization algorithm for quality requirements and cache. S52: Use dynamic programming algorithm to solve the user quality requirement subproblem and determine the number of layers of user requests for video content; S53: The cache placement subproblem is solved by using a many-to-many matching algorithm based on matching theory, and the video content is cached and placed.

6. The content caching method based on multi-network channel transmission according to claim 5, characterized in that: In step S52, a dynamic programming algorithm is used to solve the user quality requirement subproblem. The determination of the number of video content layers requested by the user specifically includes defining a rate threshold as the basis for the user to select the number of layers, discretizing it into multiple rate intervals through rate division, using a dynamic programming algorithm to solve for the latency satisfaction obtained by requesting the highest level of video content within a certain rate interval, and obtaining the maximum latency satisfaction through recursive calculation, thereby determining the corresponding rate interval and the number of video layers; that is, the number of video layers provided to the user in a certain rate interval.

7. The content caching method based on multi-network channel transmission according to claim 5, characterized in that: The method employs a many-to-many matching algorithm based on matching theory to solve the cache placement subproblem. The cache placement of video content includes establishing an initial matching state between the small base station and the enhancement layer, and then determining the cache location of a certain enhancement layer within the small base station. Latency satisfaction with the cache and based on enhancement layer Latency satisfaction rate cached in a small base station Separate priority lists are established for small base stations and enhancement layers. Each enhancement layer sends a request to the small base station with the highest priority in its priority list. Each small base station accepts the highest priority enhancement layer from its priority list. For sets of small base stations with disjoint priority lists... and enhancement layer content , respectively defined as disjoint non-empty sets and Many-to-many matching is a set. and Among them, at least one one of the elements Can and elements in Matching; pairing is performed using the swap matching function; until the small base station's buffer space is completely used up or the unmatched enhancement layer is rejected by all small base stations.

8. The content caching method based on multi-network channel transmission according to claim 7, characterized in that: The exchange matching function is expressed as follows: ， ， In the formula, Indicates small base station Enhanced layer content With small base stations Enhanced layer content The exchange matching function; This indicates the removal of small base stations. and enhancement layer content Pairing with small base stations and enhancement layer content Pairing; This indicates the removal of small base stations. and enhancement layer content Pairing; This indicates the removal of small base stations. and enhancement layer content Pairing; Indicates small base station The enhanced layer content collection, Indicates small base station The enhanced layer content set; given a pair ,when satisfy When a swapping blockage occurs, the two SBSs, during the selection of cached content, swap to increase the overall latency satisfaction until no more swapping blockages exist in the system, at which point the matching reaches a stable state. Indicates small base station Enhanced layer content With small base stations Enhanced layer content Pairing between them.

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