Video cache control method and system, electronic equipment and medium

Abstract

The invention provides a video caching control method and system, electronic equipment and a storage medium, and aims to solve the problems of traffic waste and unstable downloading speed caused by frequent dragging of a progress bar and re-caching of a video, the method comprises the following steps: adopting a multi-level cache region for an intelligent terminal, and dividing the multi-level cache region into a basic cache region and a prediction cache region, respectively corresponding to a currently played video clip cache and a to-be-played clip cache of a dragged video; dynamically calculating the cache size of a basic cache region of the intelligent cache region according to the video code rate, the signal condition and the cache time; determining a dynamic adjustment factor according to the watching habit parameter and the network condition parameter so as to determine a cache amount of a prediction cache region; and according to the video code rate, the dynamic adjustment factor and the future watching time when the video is predicted to be frequently dragged, calculating the cache size of a prediction cache region of the intelligent cache region. According to the method and the device, unnecessary cache flow can be reduced, smooth watching experience can be ensured, and the user satisfaction degree is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of communication and data cache, and in particular to a video cache control method, a video cache control system, an electronic device and a computer-readable storage medium. Background Art

[0002] With the rapid development of mobile communication technology, especially the commercialization of 5G technology, the bandwidth and data transmission speed of mobile Internet services have been greatly improved, and users' demand for high-definition video and streaming content has also increased. Mainstream video websites cache videos. There are two main caching strategies: Full caching method: Before the video is played, the entire video file is cached first, and the playback starts after the cache is completed. This method can ensure that there will be no lag during playback. Fixed-length caching method: Set a fixed cache area, and start playing when the cached data reaches a certain time length; during playback, if the cached data is lower than the set value, continue caching until the threshold is reached.

[0003] In the existing video streaming caching solution, there are several major problems: Frequent dragging of the progress bar leads to traffic waste: Many users will drag the progress bar when watching a video out of curiosity or to understand the fun of the video, which will cause the video to be re-cached, resulting in unnecessary traffic waste, especially in the 5G network environment, where the bandwidth is high and the caching speed is extremely fast, but if the user only watches a small part of the video, the rest of the data will be wasted. This will lead to user complaints, negative attitudes towards operators, and even reluctance to use 5G. In addition, the load and channel quality of the 5G network is an unstable factor. Network fluctuations may lead to unstable download speeds, which will affect the execution of video caching strategies. Summary of the invention

[0004] In order to at least solve the problem that the progress bar is frequently dragged in the existing video streaming media caching solution in the prior art, the video needs to be re-cached resulting in traffic waste, and network fluctuations may cause unstable download speed. The present disclosure provides a video cache control method, a video cache control system, an electronic device, and a computer-readable storage medium; which can not only reduce unnecessary cache traffic, but also ensure a smooth viewing experience, thereby making full use of the high bandwidth advantage of the 5G network, optimizing video playback performance, and improving user satisfaction.

[0005] In a first aspect, the present disclosure provides a video cache control method, the method comprising:

[0006] A multi-level cache area is used for smart terminals, which is divided into a basic cache area and a prediction cache area, corresponding to the cache of the currently playing video segment and the cache of the segment of the dragged video to be played;

[0007] Dynamically calculate the cache size of the basic cache area of ​​the intelligent cache area according to the video bit rate, signal conditions and cache time;

[0008] Determining a dynamic adjustment factor according to viewing habit parameters and network condition parameters to determine a cache amount in a predicted cache area;

[0009] The cache size of the predicted cache area of ​​the smart cache area is calculated according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged.

[0010] Furthermore, the cache size of the basic cache area of ​​the intelligent cache area is dynamically calculated according to the video bit rate, signal condition and cache time, including:

[0011] The cache volume B of the basic cache area is calculated by the following formula init :

[0012]

[0013] And calculate f(RSRP, SINR) by the following formula:

[0014]

[0015] Where M is the video bit rate, and the signal condition is determined by f(RSRP, SINR). f(RSRP, SINR) is a function calculated based on signal quality RSRP (Reference Signal Receiving Power) and SINR (Signal to Interference plus Noise Ratio), which describes the impact of signal quality on the cache capacity. RSRP thresh and SINR thresh are the thresholds of RSRP and SINR, β1 and β2 are adjustment coefficients used to adjust the sensitivity of the function to signal quality, and α is a constant coefficient used to further adjust the change of the buffer amount under different signal qualities.

[0016] Furthermore, the calculation of the cache size of the predicted cache area of ​​the smart cache area according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged includes:

[0017] The predicted buffer size B is calculated by the following formula pred :

[0018]

[0019] Among them, T predTo predict the future viewing time when frequent video dragging occurs, α(Hu, N) is a dynamic adjustment factor that depends on the user interest factor H u and the network condition parameter N, which is used to determine the cache capacity of the prediction buffer area.

[0020] Furthermore, determining the dynamic adjustment factor according to the viewing habit parameter and the network condition parameter includes:

[0021] The dynamic adjustment factor α(Hu, N) is calculated by the following formula:

[0022]

[0023] Among them, H u is the user interest factor, which indicates the user's interest in the current video, ranging from 0 to 1. The coefficient δ represents the control of H u The impact on the amount of buffering, CQI (Channel Quality Indicator, channel quality indicator) indicates the network quality, CQI max represents the maximum CQI value, and β represents the CQI adjustment coefficient.

[0024] Furthermore, the method further comprises:

[0025] Collect historical behavior characteristics of users. For each video type, collect the following user behavior data: drag frequency F drag , drag interval T drag , drag jump distance L drag , Viewing completion degree C complete ;

[0026] Based on the collected historical behavior feature data of users, the user interest factor H of the user for the video is predicted by considering the characteristics of different video types and user behavior through the preset model. u :

[0027] H u =σ(γ1·f type (T)+γ2·f drag (F drag , T drag , L drag )+γ3·f completion (C complete ))

[0028] Among them, σ(x) is the Sigmoid function, which maps the score to the interval [0, 1]. γ1, γ2, and γ3 are adjustment coefficients, which respectively control the influence of video type, dragging behavior, and viewing completion on the interest score. The video type factor f type (T): represents the impact of video type on user interest;

[0029] Drag behavior factor f drag (F drag , T drag , L drag ): Indicates the effect of drag frequency, drag interval, and drag jump distance on interest, calculated by the following formula:

[0030]

[0031] α1, α2, and α3 are adjustment coefficients that control the impact of various factors on interest scores;

[0032] Viewing completion factor f completion (C complete ): Indicates the impact of the user's viewing completeness on their interest in the video, calculated using the following formula:

[0033]

[0034] C complete is the degree of completion of the video watched by the user (between 0 and 1). If the user watches the video completely, C complete =1.

[0035] Furthermore, the method further comprises:

[0036] Adjust the video download rate V according to the difference between the cache amount and the playback demand adjust ;include:

[0037] When the cache size C current Reaching threshold B thresh When , the adjusted video download rate V is calculated by the following formula adjust :

[0038]

[0039] Among them, M is the video playback bit rate, C remaining is the remaining cache size, C total is the total cache capacity of the cache area, V init It is the initial download rate of the terminal given by the base station according to the CQI of the user terminal and the load of the base station.

[0040] Furthermore, the method further comprises:

[0041] When it is detected that the user has not performed any dragging behavior within a certain period of time and the cache area starts to cache new videos, it switches to the basic cache area and sends the event to the base station, so that the base station automatically restores the terminal to a normal download rate.

[0042] In a second aspect, the present disclosure provides a video cache control system, the system comprising:

[0043] A cache setting module is configured to use a multi-level cache area for the smart terminal, which is divided into a basic cache area and a prediction cache area, corresponding to the cache of the currently played video segment and the cache of the segment of the dragged video to be played;

[0044] A basic buffer area caching module, which is configured to dynamically calculate the cache size of the basic buffer area of ​​the intelligent buffer area according to the video bit rate, signal conditions and cache time;

[0045] A prediction buffer area caching module, which is configured to determine a dynamic adjustment factor based on viewing habit parameters and network condition parameters to determine a cache amount of the prediction buffer area; and

[0046] The cache size of the predicted cache area of ​​the smart cache area is calculated according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged.

[0047] In a third aspect, the present disclosure provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the video cache control method as described in any one of the first aspects.

[0048] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the video cache control method described in any one of the first aspects is implemented.

[0049] Beneficial effects:

[0050] The video cache control method, video cache control system, electronic device and storage medium provided by the present disclosure; by setting a basic cache area and a predicted cache area, corresponding to the cache of the currently played video segment and the cache of the segment to be played by dragging the video, respectively, using a dynamic adjustment cache strategy, through real-time analysis of the user's viewing behavior, network status, and video content characteristics, to achieve accurate cache control, reduce the traffic waste caused by the dragging of the video progress bar and frequent jumps, and excessive cached videos, and at the same time optimize the cache rate through real-time network status feedback to ensure a smooth viewing experience. It can not only reduce unnecessary cache traffic, but also make full use of the high bandwidth advantage of the 5G network to optimize video playback performance and improve user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a flow chart of a video cache control method provided in the first embodiment of the present disclosure;

[0052] Figure 2 This is an architecture diagram of a video cache control system provided in Embodiment 3 of the present disclosure;

[0053] Figure 3 This is an architecture diagram of an electronic device provided in Embodiment 4 of the present disclosure. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the present disclosure is further described in detail below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention, rather than to limit the present invention.

[0055] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence; and, in the absence of conflict, the embodiments in the present disclosure and the features in the embodiments can be arbitrarily combined with each other.

[0056] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The singular forms of "a", "said" and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings.

[0057] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present disclosure, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0058] The technical solution of the present invention and how the technical solution of the present invention solves the technical problems existing in the prior art are described in detail below with specific embodiments. It is understandable that in the embodiments of the present application, the execution subject can perform some or all of the steps in the embodiments of the present application, and these steps or operations are only examples. The embodiments of the present application can also perform other operations or deformations of various operations. In addition, each step can be performed in a different order presented in the embodiments of the present application, and it is possible not to perform all the operations in the embodiments of the present application. And, the following several specific embodiments can be combined with each other, and may not be repeated in some embodiments for the same or similar concepts or processes.

[0059] Figure 1 A flow chart of a video cache control method provided in the first embodiment of the present disclosure is shown in FIG. Figure 1 As shown, the method includes:

[0060] Step S101: a multi-level cache area is used for the smart terminal, which is divided into a basic cache area and a prediction cache area, corresponding to the cache of the video segment currently being played and the cache of the segment of the dragged video to be played;

[0061] Step S102: dynamically calculating the buffer size of the basic buffer area of ​​the intelligent buffer area according to the video bit rate, signal condition and buffer time;

[0062] Step S103: determining a dynamic adjustment factor according to the viewing habit parameter and the network condition parameter, so as to determine the cache amount of the predicted cache area;

[0063] Step S104: Calculate the cache size of the predicted cache area of ​​the smart cache area according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged.

[0064] The purpose of the embodiments of the present disclosure is to achieve accurate cache rate control by real-time analysis of user viewing behavior, network status, and video content characteristics; reduce unnecessary traffic waste, while improving resource utilization of 5G networks to ensure a smooth viewing experience for users.

[0065] The intelligent terminal mentioned in the present disclosure may be a smart phone (such as an Android phone, an IOS phone, etc.), a tablet computer, a laptop computer, a smart device, etc. Specifically, during the caching process, the intelligent terminal uses a multi-level cache area, which is divided into a "basic cache area" and a "predictive cache area". They correspond to the cache of the currently playing video segment and the segment to be played by dragging the video, respectively, so as to achieve precise control, and both are intelligent mixed access.

[0066] Basic buffer area: To ensure smooth video playback, it is used to store videos that users watch stably and ensure smooth video playback. Stable viewing can be defined as no dragging events for x time. The size of the basic buffer area is dynamically calculated based on the real-time network environment:

[0067] Prediction cache area: used to cache content that may be needed in the future when the video is frequently dragged. Frequent dragging is defined as continuous dragging more than n times in a short period of time (such as 15 seconds), such as n is 2 or 3, etc., which means that the user is curious about the video or wants to know whether the video is interesting.

[0068] For the basic cache area, when a user plays an online video through an App on a smart terminal, the cache size is determined by obtaining basic information of the video played on the smart terminal, including parameters such as video bit rate M and cache time length T.

[0069] Video bit rate M (unit: Mbps): obtained by adding the audio bit rate and video bit rate of the video content, used to calculate the amount of data corresponding to the video per second.

[0070] Total video duration (T total ): Total duration of the video (in seconds).

[0071] Cache time T init (Unit: seconds): To ensure smooth video playback, the system will pre-cache data according to the set cache time, usually set to 1-5 minutes. The cache time length T is set according to the policy of the video service provider.

[0072] Network signal status (Q): Smart terminals can obtain the network signal strength RSRP and quality SINR of the current base station in real time. RSRP is used to measure the reception strength of wireless signals, in dBm. The smaller the value, the worse the signal. SINR is used to measure the ratio of signal to noise, in dB. The lower the value, the worse the signal. These two parameters can also determine the adjustment of the initial cache size of video playback.

[0073] For the prediction cache area, first determine the dynamic adjustment factor based on the viewing habit parameters and network condition parameters to determine the cache capacity of the prediction cache area; then calculate the cache capacity of the prediction cache area of ​​the smart cache area based on the video bit rate, dynamic adjustment factor and predicted future viewing time when frequent video dragging occurs. In this way, the user's interest score for the video being watched is predicted based on the user's historical behavior habits, and the cache size is dynamically adjusted based on the interest score, ultimately making the video playback smoother and reducing traffic waste.

[0074] The basic cache is used to cache video clips for stable viewing. The predictive cache is used to determine when the user frequently drags the progress bar to cache videos. When dragging videos, the videos in the basic cache can be cleared first. In this way, the multi-level cache strategy (basic cache and predictive cache) reduces the frequent clearing of the cache and allows personalized adjustments based on user viewing habits. By setting different caches to meet different content needs, resource utilization efficiency can be improved.

[0075] The disclosed embodiment sets a basic cache area and a prediction cache area, which correspond to the cache of the currently played video segment and the cache of the segment to be played by dragging the video, respectively, and uses a dynamic adjustment cache strategy to realize accurate cache control by real-time analysis of the user's viewing behavior, network status, and video content characteristics, thereby reducing the traffic waste caused by dragging the video progress bar, frequent jumping, and excessive caching of videos. At the same time, through the feedback of real-time network conditions, the cache rate is optimized to ensure a smooth viewing experience. It can not only reduce unnecessary cache traffic, but also make full use of the high bandwidth advantage of the 5G network to optimize video playback performance and improve user satisfaction.

[0076] Furthermore, the cache size of the basic cache area of ​​the intelligent cache area is dynamically calculated according to the video bit rate, signal condition and cache time, including:

[0077] The cache volume B of the basic cache area is calculated by the following formulainit :

[0078]

[0079] And calculate f(RSRP, SINR) by the following formula:

[0080]

[0081] Where M is the video bit rate, and the signal condition is determined by f(RSRP, SINR). f(RSRP, SINR) is a function calculated based on signal quality RSRP and SINR, which describes the impact of signal quality on the buffer size. RSRP thresh and SINR thresh are the thresholds of RSRP and SINR, β1 and β2 are adjustment coefficients used to adjust the sensitivity of the function to signal quality, and α is a constant coefficient used to further adjust the change of the buffer amount under different signal qualities.

[0082] According to the video bit rate M and signal condition Q, the cache size B of the basic cache area of ​​the intelligent cache area is dynamically calculated init . Set a cache time T init , then the cache capacity of the basic cache area is B init The calculation formula is:

[0083]

[0084] in:

[0085] 8 refers to 1MBps=8Mbps,

[0086] f(RSRP, SINR) is a function calculated based on signal quality (RSRP and SINR), which describes the impact of signal quality on the cache size. The worse the RSRP and SINR, the slower the download speed, which may cause the cache speed to be slower than the playback speed, insufficient cache data, and cause freezes. Therefore, it is necessary to ensure that the cache size of the basic cache area is increased when the signal quality is poor, and the cache size of the basic cache area is reduced when the signal is good. The cache size of the basic cache area can be intelligently adjusted according to different network qualities to achieve intelligent caching. The function can be designed as:

[0087]

[0088] Among them: RSRP thresh and SINR threshThey are the thresholds of RSRP and SINR, respectively. When the signal quality is lower than the threshold, the system needs to increase the cache capacity. The threshold can be set according to the actual situation, such as according to the video playback requirements. β1 and β2 are adjustment coefficients used to adjust the sensitivity of the function to signal quality. α is a constant coefficient used to further adjust the change of cache capacity under different signal qualities.

[0089] When the network signal quality is good (RSRP and SINR are high), the value of f(RSRP, SINR) is close to 0, and the basic buffer size is close to the standard value M*Tinit / 8. When the signal quality is poor (RSRP and SINR are low), the value of f(RSRP, SINR) increases, and the basic buffer size increases accordingly, ensuring that the video can be played smoothly even in a poor network environment.

[0090] For example, in one embodiment:

[0091] Assume that the bit rate of a video is M = 4Mbps, the video duration is 120 minutes, and the cache time is Tinit = 60 seconds (1 minute). Set the RSRP of the network signal to -110dBm, SINR to 12dB, and set the threshold and adjustment coefficient as follows:

[0092] RSRP thresh =-100, SINR thresh =15dB, β1=0.05, β2=0.1, α=0.2

[0093] First calculate f(RSRP, SINR):

[0094]

[0095] f(RSRP,SINR)≈0.377·0.574=0.216

[0096] Then, calculate the cache volume B of the basic cache area init :

[0097] B init =4*60 / 8*(1+0.2*0.216)=30*(1+0.0432)=30*1.0432=31.296MB,

[0098] In this case, the cache capacity of the basic buffer area is about 187.776MB. Taking into account the poor signal quality (RSRP=-110dBm and SINR=12dB), the cache capacity of the basic buffer area has been significantly increased to ensure that the video playback will not be stuck even if the download speed is slow.

[0099] Furthermore, the calculation of the cache size of the predicted cache area of ​​the smart cache area according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged includes:

[0100] The predicted buffer size B is calculated by the following formula pred :

[0101]

[0102] Among them, T pred To predict the future viewing time when frequent video dragging occurs, α(Hu, N) is a dynamic adjustment factor that depends on the user interest factor H u and the network condition parameter N, which is used to determine the cache capacity of the prediction buffer area.

[0103] The size of the prediction buffer is determined by predicting the future viewing time and user interest factor when frequent video dragging occurs. When the user's interest is high and the possible viewing time is longer, the prediction buffer size is larger.

[0104] Furthermore, determining the dynamic adjustment factor according to the viewing habit parameter and the network condition parameter includes:

[0105] The dynamic adjustment factor α(Hu, N) is calculated by the following formula:

[0106]

[0107] Among them, H u is the user interest factor, which indicates the user's interest in the current video, ranging from 0 to 1. The coefficient δ represents the control of H u The impact on the amount of buffering, the channel quality indicator CQI indicates the network quality, CQI max represents the maximum CQI value, and β represents the CQI adjustment coefficient.

[0108] The predicted buffer area is based on the viewing habit parameter H u and network status, caching potentially needed data. u When the value is high, the size of the prediction buffer will increase accordingly to ensure that users can smoothly watch content for a longer time; u When it is lower, the cache size will be reduced, thus saving traffic resources. The specific method is as follows:

[0109]

[0110] Where T pred Predicted how long the user will watch after each drag event if frequent drag events occur.

[0111] The expression of α(Hu, N) is as follows

[0112]

[0113] Among them, the user interest factor H u : User's interest in the current video, ranging from 0 to 1. The higher the value, the higher the user's interest, and the buffer area should be appropriately increased. For example, when the user is more interested in watching (H u >0.7), the smart terminal will increase the prediction buffer area B pred , so as to cache more video content in advance and ensure the smoothness of continuous viewing. u <0.3), the smart terminal will reduce the prediction cache area and only cache a small amount of data to save traffic resources.

[0114] Coefficient δ: Control H u The degree of influence on the cache amount. The larger the value, the more obvious the influence of user interest on the cache.

[0115] CQI (Channel Quality Indicator): Channel quality indication. The higher the value, the better the network quality.

[0116] Maximum CQI value CQI max : It can be set to a certain standard value (such as 30). When the CQI is lower, the channel quality is poor, and the buffer area increases accordingly.

[0117] CQI adjustment coefficient β: controls the degree of influence of channel quality on the buffer size. The larger the value, the more obvious the influence of channel quality on the buffer.

[0118] When user interest is high (H u close to 1) or poor channel quality (CQI is much lower than CQI max ), α(H u , N) increases, indicating that the cache size should be increased to cope with potential network fluctuations and high viewing demand. When user interest is low and channel quality is good, α(H u , N) is close to 1, indicating that too much cache is not needed.

[0119] Assumptions:

[0120] User Interests u =0.7

[0121] Current CQI = 15, CQI max =30

[0122] Set δ = 0.3, β = 0.5

[0123] α(H u , N)=1+0.3*0.7+0.5*(1-15 / 30)=1.46

[0124] In this case, the cache capacity of the prediction buffer will increase by about 46%, thereby ensuring smooth video playback under conditions of high interest and average channel quality.

[0125] Furthermore, the method further comprises:

[0126] Collect historical behavior characteristics of users. For each video type, collect the following user behavior data: drag frequency F drag , drag interval T drag , drag jump distance L drag , Viewing completion degree C complete ;

[0127] Based on the collected historical behavior feature data of users, the user interest factor H of the user for the video is predicted by considering the characteristics of different video types and user behavior through the preset model. u :

[0128] H u =σ(γ1·f type (T)+γ2·f drag (F drag , T drag , L drag )+γ3·f completion (C complete ))

[0129] Among them, σ(x) is the Sigmoid function, which maps the score to the interval [0, 1]. γ1, γ2, and γ3 are adjustment coefficients, which respectively control the influence of video type, dragging behavior, and viewing completion on the interest score. The video type factor f type (T): represents the impact of video type on user interest;

[0130] Drag behavior factor f drag (F drag , T drag , L drag ): Indicates the effect of drag frequency, drag interval, and drag jump distance on interest, calculated by the following formula:

[0131]

[0132] α1, α2, and α3 are adjustment coefficients that control the impact of various factors on interest scores;

[0133] Viewing completion factor f completion (C complete ): Indicates the impact of the user's viewing completeness on their interest in the video, calculated using the following formula:

[0134]

[0135] C complete is the degree of completion of the video watched by the user (between 0 and 1). If the user watches the video completely, C complete =1.

[0136] Predict the user's interest factor H for the video u The purpose is to predict the user's interest score in the video they are watching based on their historical behavior habits, and dynamically adjust the cache size based on the interest score, ultimately making the video playback smoother and reducing traffic waste.

[0137] To effectively predict the user's interest score for the current video based on the user's historical behavior, the following key historical behavior features need to be considered, especially those related to dragging behavior:

[0138] Video type: (documentary, movie, TV series, variety show, short video, animation...) Each video type has different appeal to users, and users will behave differently when watching different types of videos.

[0139] For each type, collect the following user behavior data

[0140] Drag frequency (F drag ):

[0141] The frequency at which the user drags the progress bar during video playback (unit: times / minute). A high dragging frequency usually indicates that the user is not interested or is quickly browsing the video.

[0142] Drag interval (T drag ):

[0143] The time interval between continuous dragging (in seconds). If the dragging interval is short, it means that the user is quickly browsing the video and may be looking for the part of interest; if the interval is long, it means that the user is watching a certain part.

[0144] Drag jump distance (L drag ):

[0145] The jump distance of each drag (in seconds). A larger jump distance indicates that the user is skipping some parts and is usually less interested in the content; a smaller jump distance indicates that the user may be interested in some fragments.

[0146] Viewing completion (C complete ):

[0147] The user's past viewing completeness (video progress viewing ratio reaches 80%). A high viewing history usually means that the user is more interested in the video.

[0148] Based on the collected data, a model is designed to predict the user's interest score H for the video by considering the characteristics of different video types and user behavior. u :

[0149] H u =σ(γ1·f type (T)+γ2·f drag (F drag , T drag , L drag )+γ3·f completion (C complete ))

[0150] σ(x) is the Sigmoid function, which maps the score to the interval [0, 1].

[0151] γ1, γ2, and γ3 are adjustment coefficients that respectively control the influence of video type, dragging behavior, and viewing completion on the interest score.

[0152] Video type factor f type (T): indicates the impact of video type on user interest. Different video types have different impacts on user interest. For example, movies are usually more attractive to users, while advertisements may be less attractive. A basic interest factor is assigned to each video type, such as in one embodiment, which can be set as:

[0153] Documentary: f type (documentary) = 0.8

[0154] Movie: f type (Movie) = 1.0

[0155] TV series: f type (TV series) = 0.9

[0156] Variety show:f type (Variety show) = 1.2

[0157] Short video:f type (Short video) = 0.7

[0158] Animation: f type (animation) = 1.1

[0159] Drag behavior factor f drag (F drag , T drag , L drag ): represents the effects of drag frequency, drag interval, and drag jump distance on interest.

[0160]

[0161] α1, α2, and α3 are adjustment coefficients that control the impact of various factors on interest scores.

[0162] Viewing completion factor f completion (C complete ): Indicates the impact of the user's completeness of watching a video on their interest.

[0163]

[0164] C complete is the degree of completion of the video watched by the user (between 0 and 1). If the user watches the video completely, C complete =1, indicating high interest.

[0165] Collect user behavior feature data as model input, make predictions, and obtain the user viewing interest parameter H u As the basis for adjusting cache strategy.

[0166] Furthermore, the method further comprises:

[0167] Adjust the video download rate V according to the difference between the cache amount and the playback demand adjust ;include:

[0168] When the cache size C current Reaching threshold B thresh When , the adjusted video download rate V is calculated by the following formula adjust :

[0169]

[0170] Among them, M is the video playback bit rate, C remaining is the remaining cache size, C total is the total cache capacity of the cache area, V init It is the initial download rate of the terminal given by the base station according to the CQI of the user terminal and the load of the base station.

[0171] The disclosed embodiment also dynamically adjusts the download rate according to the real-time network conditions (such as the download rate provided by the base station) and the cache amount. When the cache amount in the cache area reaches a threshold, the download rate can be reduced to avoid unnecessary traffic waste, while ensuring that the cached data is sufficient to meet user needs during video playback to avoid freezes.

[0172] Specific process: When caching starts, the terminal will send relevant information to the base station to notify the base station of the caching event. At the same time, the terminal will also send the measured channel quality (CQI) to the base station. The base station will determine the initial download rate of the terminal based on the CQI (channel quality indicator) of the user terminal and the load of the base station. Assume that the initial rate given by the base station is V init . Note that V initIt is determined by the base station based on its own load, CQI and other factors. This value will change based on its own load, CQI and other factors.

[0173] At this speed, the video cached in the buffer reaches a certain threshold B thresh , indicating that the current cache size is sufficient to meet the needs of video playback, and you can start to slow down the download.

[0174] The threshold can be set based on the clarity of the video content. For example, if the video has a higher definition, the cache threshold should be set larger to ensure that there is enough pre-cache during the video playback. Specifically:

[0175] 1. The definition of the video is divided into: such as 360p, 480p, 720p, 1080p, 2K, 4K, etc. These numbers represent the resolution of the video, that is, the number of pixels in the video. Generally speaking, the more pixels there are, the higher the quality of the video. The bit rate M of videos of different definitions is also different.

[0176] Set the cache threshold B based on experience thresh : Through experience and the association of video types, we can calculate the bit rate M and T of the video (if the basic buffer is used, T = T init , if the prediction buffer is used, T = T pred ) to roughly set a preliminary range of the cache threshold. Depending on the type of video, the cache threshold can be adjusted in the following proportions:

[0177] 360p: Usually the initial threshold of caching can be set to 10%-20% of the total duration of T, that is:

[0178] B thresh =M×T×factor, factor=0.1

[0179] For example, for a 360p video with a T of 3 minutes and a bit rate of 1 Mbps, the cache threshold is approximately:

[0180] B thresh =1×180×0.1=18MB

[0181] 480p: Usually the initial threshold for caching can be set to 20% of the total duration of T:

[0182] B thresh =M×T×factor, factor=0.2

[0183] 720p: Usually the initial threshold for caching can be set to 30% of the total duration of T:

[0184] B thresh =M×T×factor, factor=0.3

[0185] 1080p: Usually the initial threshold for caching can be set to 40% of the total duration of T:

[0186] 2K: Usually the initial threshold of the cache can be set to 50% of the total duration of T:

[0187] 4K video: Due to the higher bitrate of HD and 4K videos, more cache is usually required to avoid lag. For 4K videos, the cache threshold can be set to 60% of the T video length.

[0188] When the cache size C current Reaching threshold B thresh When the system adjusts the download rate V according to the difference between the cache amount and the playback demand adjust , the system will gradually reduce the download rate. So how to set V adjust , specifically:

[0189] Assume that the remaining cache size is C remaining , the total cache capacity of the cache area is C total , the current cache size is C current .

[0190] The ratio of the remaining cache to the total cache capacity is:

[0191]

[0192] According to this ratio, the download rate V adjust To ensure that the download rate is adjusted proportionally to the remaining cache amount but not lower than the video playback bitrate M, we can design the following adjustment formula:

[0193]

[0194] The download rate will decrease in proportion to the remaining cache size, but will not be lower than the video playback bitrate M, ensuring that even if the cache space is small, the download rate can meet the video playback requirements.

[0195] Application examples:

[0196] Assumptions:

[0197] Initial download rate V init =50Mbps.

[0198] Current cache size C current =2000MB.

[0199] Total cache capacity C total =3000MB.

[0200] Video bit rate M = 8 Mbps (ie, the download rate cannot be lower than 8 Mbps).

[0201] Remaining cache size C remaining =C total -C current =3000-2000=1000

[0202] C remaining / C total =0.333

[0203] Adjusted download rate: V adjust =max(50×0.3333,8), so V adjust 16.65Mbps

[0204] In this case, the remaining cache capacity is 33.33%, and the download rate is adjusted to about 16.67Mbps. Assuming that the cache capacity continues to decrease, the current cache capacity C current =2900MB, the remaining cache size is C remaining =100MB. remaining / C total =0.0333

[0205] V adjust =max(50×0.0333,8), so V adjust Yes8Mbps

[0206] Furthermore, the method further comprises:

[0207] When it is detected that the user has not performed any dragging behavior within a certain period of time and the cache area starts to cache new videos, it switches to the basic cache area and sends the event to the base station, so that the base station automatically restores the terminal to a normal download rate.

[0208] The disclosed embodiment also designs an automatic speed limit recovery algorithm. When it is detected that the user has not performed dragging behavior for a long time (a drag detection threshold J seconds is set, which can be set to 30 seconds, 40 seconds or other values) and the cache area starts to cache new videos, the event is sent to the base station, and the base station automatically recovers to the normal speed.

[0209] Assume that the time when the speed limit starts is t0. If Δt ≥ J, the original speed V is restored. restore :V restore =V init , where Δt=t-t0.

[0210] The whole process is as follows: the user opens the app to watch the video, uses the basic cache area for caching, and calculates the size of the basic cache area according to the basic cache area capacity algorithm. At the same time, the download speed is downloaded according to the normal speed provided by the base station as Vinit , and the cache amount of the basic cache area reaches the threshold B thresh , the event is sent to the base station, and the base station starts to limit the speed of the user terminal. The speed limit is:

[0211]

[0212] If the video is dragged once, it is still cached in the basic buffer area. If the user drags the video twice within 15 seconds, the system considers that frequent dragging events have occurred, and the predicted buffer area is used to cache the video. At the same time, the size of the predicted buffer area is calculated according to the predicted buffer area capacity algorithm. At the same time, the download speed is downloaded as Vinit according to the normal speed provided by the base station. When the cache capacity of the predicted buffer area reaches the threshold B, the system will automatically start to download the video. thresh , the event is sent to the base station, and the base station starts to limit the speed of the user terminal. The speed limit is:

[0213]

[0214] When frequent drag events occur, if it is detected that the user has not performed dragging behavior for a long time (a drag detection threshold J seconds is set), the basic buffer area is switched to the basic buffer area, and the event is sent to the base station to start caching new video clips. The base station automatically returns to the normal rate V restore =V init .

[0215] The disclosed embodiment reduces the traffic waste caused by video progress bar dragging and frequent jumping, and excessive video caching by dynamically adjusting the cache strategy and intelligently adjusting the rate. At the same time, it optimizes the cache rate through real-time network status feedback to ensure a smooth viewing experience. This method has strong flexibility and adaptability, not only can it reduce unnecessary cache traffic, but also can make full use of the high bandwidth advantage of 5G networks, optimize video playback performance, and improve user satisfaction.

[0216] The second embodiment of the present disclosure also provides a video cache control system, which realizes accurate cache rate control by real-time analysis of user viewing behavior, network status, and video content characteristics; reduces unnecessary traffic waste, while improving the resource utilization of 5G networks, and ensures a smooth viewing experience for users. The system consists of 4 modules:

[0217] 1. Video information acquisition and intelligent cache setting module

[0218] During the caching process, the module uses a multi-level cache area for the smart terminal, which is divided into a "basic cache area" and a "prediction cache area". They correspond to the cache of the currently playing video segment and the segment to be played by dragging the video, respectively, so as to achieve precise control, and both are intelligent mixed access.

[0219] 2. User video interest prediction and analysis module

[0220] The goal of this module is to predict the user's interest score in the video they are watching based on their historical behavior habits, and dynamically adjust the cache size based on the interest score, ultimately making the video playback smoother and reducing traffic waste.

[0221] 3. Download rate adjustment module

[0222] Main idea: This module dynamically adjusts the download rate according to the real-time network conditions (such as the download rate provided by the base station) and the cache amount. When the cache amount in the cache area reaches a threshold, the download rate can be reduced to avoid unnecessary traffic waste, while ensuring that the cached data is sufficient to meet user needs during video playback to avoid lag.

[0223] 4. Automatic speed limit recovery module

[0224] Description: An automatic speed limit recovery algorithm is designed. When the module detects that the user has not performed dragging behavior for a long time (a drag detection threshold of J seconds is set) and the buffer area starts to cache new videos, the event is sent to the base station, and the base station automatically recovers to the normal rate.

[0225] The disclosed embodiments can reduce unnecessary traffic waste, while improving resource utilization of 5G networks, ensuring users' smooth viewing experience, and can more scientifically and efficiently manage and optimize mobile communication networks, effectively respond to network challenges, reduce user complaints, and thereby improve overall operational efficiency and service quality.

[0226] The third embodiment of the present disclosure also provides a video cache control system, such as Figure 2 As shown, the system comprises:

[0227] A cache setting module 11 is configured to use a multi-level cache area for the smart terminal, which is divided into a basic cache area and a prediction cache area, corresponding to the cache of the currently played video segment and the cache of the segment of the dragged video to be played;

[0228] A basic buffer area caching module 12, which is configured to dynamically calculate the cache size of the basic buffer area of ​​the intelligent buffer area according to the video bit rate, signal conditions and cache time;

[0229] A prediction buffer area caching module 13, which is configured to determine a dynamic adjustment factor according to a viewing habit parameter and a network condition parameter, so as to determine a cache amount of the prediction buffer area; and

[0230] The cache size of the predicted cache area of ​​the smart cache area is calculated according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged.

[0231] Furthermore, the basic buffer area cache module 12 is specifically configured as follows:

[0232] The cache volume B of the basic cache area is calculated by the following formula init :

[0233]

[0234] And calculate f(RSRP, SINR) by the following formula:

[0235]

[0236] Where M is the video bit rate, and the signal condition is determined by f(RSRP, SINR). f(RSRP, SINR) is a function calculated based on signal quality RSRP and SINR, which describes the impact of signal quality on the buffer size. RSRP thresh and SINR thresh are the thresholds of RSRP and SINR, β1 and β2 are adjustment coefficients used to adjust the sensitivity of the function to signal quality, and α is a constant coefficient used to further adjust the change of the buffer amount under different signal qualities.

[0237] Furthermore, the prediction buffer area cache module 13 is specifically configured as follows:

[0238] The predicted buffer size B is calculated by the following formula pred :

[0239]

[0240] Among them, T pred To predict the future viewing time when frequent video dragging occurs, α(Hu, N) is a dynamic adjustment factor that depends on the user interest factor H u and the network condition parameter N, which is used to determine the cache capacity of the prediction buffer area.

[0241] Furthermore, the prediction buffer area caching module 13 is also specifically configured as follows:

[0242] The dynamic adjustment factor α(Hu, N) is calculated by the following formula:

[0243]

[0244] Among them, H u is the user interest factor, which indicates the user's interest in the current video, ranging from 0 to 1. The coefficient δ represents the control of H u The impact on the amount of buffering, the channel quality indicator CQI indicates the network quality, CQI max represents the maximum CQI value, and β represents the CQI adjustment coefficient.

[0245] Furthermore, the system also includes a user interest analysis module 14;

[0246] The user interest analysis module 14 is configured to collect the user's historical behavior characteristics, and for each video type, collects the following user behavior data: drag frequency F drag , drag interval T drag , drag jump distance L drag , Viewing completion degree C complete ;as well as,

[0247] Based on the collected historical behavior feature data of users, the user interest factor H of the user for the video is predicted by considering the characteristics of different video types and user behavior through the preset model. u :

[0248] H u =σ(γ1·f type (T)+γ2·f drag (F drag , T drag , L drag )+γ3·f completion (C complete ))

[0249] Among them, σ(x) is the Sigmoid function, which maps the score to the interval [0, 1]. γ1, γ2, and γ3 are adjustment coefficients, which respectively control the influence of video type, dragging behavior, and viewing completion on the interest score. The video type factor f type (T): represents the impact of video type on user interest;

[0250] Drag behavior factor f drag (F drag , T drag , L drag ): Indicates the effect of drag frequency, drag interval, and drag jump distance on interest, calculated by the following formula:

[0251]

[0252] α1, α2, and α3 are adjustment coefficients that control the impact of various factors on interest scores;

[0253] Viewing completion factor f completion (C complete ): Indicates the impact of the user's viewing completeness on their interest in the video, calculated using the following formula:

[0254]

[0255] C completeis the degree of completion of the video watched by the user (between 0 and 1). If the user watches the video completely, C complete =1.

[0256] Furthermore, the system also includes a download rate adjustment module 15;

[0257] The download rate adjustment module 15 is configured to adjust the video download rate V according to the difference between the buffer amount and the playback demand. adjust ;include:

[0258] When the cache size C current Reaching threshold B thresh When , the adjusted video download rate V is calculated by the following formula adjust :

[0259]

[0260] Among them, M is the video playback bit rate, C remaining is the remaining cache size, C total is the total cache capacity of the cache area, V init It is the initial download rate of the terminal given by the base station according to the CQI of the user terminal and the load of the base station.

[0261] Furthermore, the system also includes an automatic speed limit recovery module 16;

[0262] The automatic speed limit recovery module 16 is configured to switch to the basic buffer area when it detects that the user has not performed any dragging behavior within a certain period of time and the buffer area starts to cache new videos, and sends the event to the base station, so that the base station automatically restores the terminal to a normal download rate.

[0263] The video cache control system of the disclosed embodiment is used to implement the video cache control method in the method embodiment 1 and the method embodiment 2, so the description is relatively simple. For details, please refer to the relevant description in the previous method embodiment, which will not be repeated here.

[0264] In addition, if Figure 3 As shown, the fourth embodiment of the present disclosure further provides an electronic device, including a memory 100 and a processor 200, wherein the memory 100 stores a computer program, and when the processor 200 runs the computer program stored in the memory 100, the processor 200 executes the above-mentioned various possible methods.

[0265] The memory 100 is connected to the processor 200. The memory 100 may be a flash memory, a read-only memory or other memory, and the processor 200 may be a central processing unit or a single-chip microcomputer.

[0266] In addition, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the above-mentioned various possible methods.

[0267] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), Digital Video Disc (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0268] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A video cache control method, characterized in that: The method comprises: A multi-level cache area is used for smart terminals, which is divided into a basic cache area and a prediction cache area, corresponding to the cache of the currently playing video segment and the cache of the segment of the dragged video to be played; Dynamically calculate the cache size of the basic cache area of ​​the intelligent cache area according to the video bit rate, signal conditions and cache time; Determining a dynamic adjustment factor according to viewing habit parameters and network condition parameters to determine a cache amount in a predicted cache area; The cache size of the predicted cache area of ​​the smart cache area is calculated according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged.

2. The method according to claim 1, characterized in that The method of dynamically calculating the cache size of the basic cache area of ​​the intelligent cache area according to the video bit rate, signal condition and cache time includes: The cache volume B of the basic cache area is calculated by the following formula init : And calculate f(RSRP, SINR) by the following formula: Where M is the video bit rate, and the signal condition is determined by f(RSRP, SINR). f(RSRP, SINR) is a function calculated based on the signal quality reference signal received power RSRP and the signal to interference plus noise ratio SINR, which describes the impact of signal quality on the buffer size. RSRP thresh and SINR thresh are the thresholds of RSRP and SINR, β1 and β2 are adjustment coefficients used to adjust the sensitivity of the function to signal quality, and α is a constant coefficient used to further adjust the change of the buffer amount under different signal qualities.

3. The method according to claim 1 or 2, characterized in that: The calculating of the predicted buffer size of the intelligent buffer according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged includes: The predicted buffer size Bpred is calculated using the following formula: Where Tpred is the predicted future viewing time when frequent video dragging occurs, and α(Hu, N) is a dynamic adjustment factor that depends on the user interest factor H. u and the network condition parameter N, which is used to determine the cache capacity of the prediction buffer area.

4. The method according to claim 3, characterized in that Determining the dynamic adjustment factor according to the viewing habit parameter and the network condition parameter includes: The dynamic adjustment factor α(Hu, N) is calculated by the following formula: Among them, H u is the user interest factor, which indicates the user's interest in the current video, ranging from 0 to 1. The coefficient δ represents the control of H u The impact on the amount of buffering, the channel quality indicator CQI indicates the network quality, CQI max represents the maximum CQI value, and β represents the CQI adjustment coefficient.

5. The method according to claim 4, characterized in that The method further comprises: Collect historical behavior characteristics of users. For each video type, collect the following user behavior data: drag frequency F drag , drag interval T drag , drag jump distance L drag , Viewing completion degree C complete ; Based on the collected historical behavior feature data of users, the user interest factor H of the user for the video is predicted by considering the characteristics of different video types and user behavior through the preset model. u : H u =σ(γ1·f type (T)+γ2·f drag (F drag ,T drag ,L drag )+γ3·f completion (C complete )) Among them, σ(x) is the Sigmoid function, which maps the score to the interval [0, 1]. γ1, γ2, and γ3 are adjustment coefficients, which respectively control the influence of video type, dragging behavior, and viewing completion on the interest score. The video type factor f type (T): represents the impact of video type on user interest; Drag behavior factor f drag (F drag , T drag , L drag ): Indicates the effect of drag frequency, drag interval, and drag jump distance on interest, calculated by the following formula: α1, α2, and α3 are adjustment coefficients that control the impact of various factors on interest scores; Viewing completion factor f completion (C complete ): Indicates the impact of the user's viewing completeness on their interest in the video, calculated using the following formula: C complete is the degree of completion of the video watched by the user (between 0 and 1). If the user watches the video completely, C complete =1.

6. The method according to claim 1, characterized in that The method further comprises: Adjust the video download rate V according to the difference between the cache amount and the playback demand adjust ;include: When the cache size C current Reaching threshold B thresh When , the adjusted video download rate V is calculated by the following formula adjust : Among them, M is the video playback bit rate, C remaining is the remaining cache size, C total is the total cache capacity of the cache area, V init It is the initial download rate of the terminal given by the base station according to the CQI of the user terminal and the load of the base station.

7. The method according to claim 6, characterized in that The method further comprises: When it is detected that the user has not performed any dragging behavior within a certain period of time and the cache area starts to cache new videos, it switches to the basic cache area and sends the event to the base station, so that the base station automatically restores the terminal to a normal download rate.

8. A video cache control system, characterized in that: The system comprises: A cache setting module is configured to use a multi-level cache area for the smart terminal, which is divided into a basic cache area and a prediction cache area, corresponding to the cache of the currently played video segment and the cache of the segment of the dragged video to be played; A basic buffer area caching module, which is configured to dynamically calculate the cache size of the basic buffer area of ​​the intelligent buffer area according to the video bit rate, signal conditions and cache time; A prediction buffer area caching module, which is configured to determine a dynamic adjustment factor based on viewing habit parameters and network condition parameters to determine a cache amount of the prediction buffer area; and The cache size of the predicted cache area of ​​the smart cache area is calculated according to the video bit rate, the dynamic adjustment factor and the predicted future viewing time when the video is frequently dragged.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the video cache control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the video cache control method according to any one of claims 1 to 7 is implemented.

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