A method for ensuring smooth online playback of mobile video and a wireless communication system

Abstract

The present invention provides a method for ensuring the smoothness of mobile video online playback. The method includes responding to mobile video freezes in real time and executing the following steps: S1, obtaining base station parameters before the mobile video freezes and the base station parameters when the mobile video freezes; S2, calculating a change vector of each base station parameter based on the obtained base station parameters, and calculating the impact of different performance impact factors on the smoothness of mobile video online playback based on a predetermined contribution vector of each base station parameter to the performance impact factor and the base station parameter change vector; S3, obtaining a QoS parameter adjustment strategy according to preset rules; S4, when the base station is controllable, adjusting the parameters according to the QoS parameter adjustment strategy obtained in step S3; when the base station is not controllable, sending a bit rate optimization strategy to mobile video users to ensure video smoothness. The present invention can use targeted strategies to adjust QoS parameters to meet the QoS requirements of different services.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, in particular to the field of mobile video online playback in the field of wireless communication technology, and more particularly to a method for ensuring the smoothness of mobile video online playback and a wireless communication system. Background Art

[0002] With the explosive growth of mobile video services, the primary form of video services has evolved from standard video on demand services to real-time, highly fluid, high-definition services with a wider range of application scenarios. Mobile video fluency, clarity, and video freeze frequency all impact the quality of experience for mobile video users. High-definition mobile video has gradually become a basic requirement for mobile video playback, and the smoothness of HD mobile video has become a major factor affecting mobile video user experience. Therefore, the quality of mobile video user experience can be enhanced by ensuring both video clarity and smoothness.

[0003] Traditional mobile video smoothness resource scheduling mechanisms primarily ensure mobile video smoothness from the user side through the core network. First, the core network's Network Data Analysis Function (NWDAF) analyzes the quality of service (QoS) requirements for mobile video services. These requirements are then transmitted to the access network via the core network's Session Management Function (SMF) and Access and Mobility Management Function (AMF). The access network then makes radio resource management decisions for the user based on these requirements. During this resource scheduling process, radio information originates from the radio network management (RNM), with information granularity at least at the minute level. This indicates that the transmission of QoS requirements through the core network requires at least tens of milliseconds of latency. Consequently, existing wireless network resource scheduling strategies are unable to guide real-time radio resource allocation, lack real-time awareness of mobile video service quality, and struggle to meet the high-quality assurance demands of mobile video users.

[0004] Mobile video fluency can also be guaranteed from the base station side. This is primarily based on QoS. QoS is implemented in mobile video base stations to improve the QoS requirements for mobile video transmission, thereby providing the basic resources for high-quality mobile video services. Since the development of 3G networks, people's demand for mobile data networks has increased. With the maturity of 4G networks and the development of 5G networks, QoS is crucial for ensuring the quality of user experience on mobile networks. For mobile video services, when the network is overloaded or congested, QoS guarantees can prevent service delays or denials, thereby improving the quality of experience for mobile video users.

[0005] However, current QoS assurance for mobile video services mostly relies on providing dedicated QoS configuration templates based on 5G QoS flows to ensure QoS requirements for mobile video services. While this approach can often meet QoS requirements based on the matching QoS template, it is relatively static. Once the corresponding service is identified, the configured resources are allocated. This can sometimes lead to different mobile video users having different QoS requirements. Some users can meet their requirements without allocating the configured resources, ensuring smooth HD mobile video playback. However, for other users, even with the resources configured according to the QoS configuration template, their QoS requirements may still not be met, causing stuttering in online mobile video playback. Consequently, this approach fails to utilize resources efficiently and effectively, nor can it fully guarantee the smoothness requirements of HD mobile video services. It is difficult to meet the real-time and precise wireless resource requirements and service quality demands of mobile video users. Therefore, how to provide real-time, dynamic QoS assurance for mobile video services based on actual network conditions and actual needs to ensure smooth mobile video playback is an urgent issue that needs to be addressed. Summary of the Invention

[0006] Therefore, the purpose of the present invention is to overcome the above-mentioned defects of the prior art and provide a method for ensuring the smoothness of mobile video online playback and a wireless communication system.

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] According to a first aspect of the present invention, a method for ensuring the smoothness of mobile video online playback is provided, the method comprising responding to mobile video freezes in real time and executing the following steps: S1, obtaining base station parameters before the mobile video freezes and the base station parameters when the mobile video freezes; S2, calculating a change vector of each base station parameter based on the obtained base station parameters, and calculating the influence of different performance influencing factors on the smoothness of mobile video online playback based on a predetermined contribution vector of each base station parameter to the performance influencing factor and the change vector of the base station parameter, wherein the performance influencing factors include: bandwidth, packet loss, delay and jitter; S3, obtaining a QoS parameter adjustment strategy according to preset rules; S4, when the base station is controllable, adjusting the parameters according to the QoS parameter adjustment strategy obtained in step S3; when the base station is not controllable, sending a bit rate optimization strategy for ensuring video smoothness to the mobile video user.

[0009] In some embodiments of the present invention, the base station parameters include: cell downlink medium access control layer transmission rate, number of downlink occupied physical resource blocks, uplink signal to interference plus noise ratio, downlink medium access control layer transmission rate, modulation and coding strategy, number of buffers occupied by packet data convergence protocol layer, number of unused buffers of packet data convergence protocol layer and / or number of downlink data packet discards.

[0010] In some embodiments of the present invention, the step S2 includes: S21, obtaining a base station parameter sample matrix, standardizing the sample matrix to obtain a covariance matrix of the sample matrix, and calculating the eigenvalues ​​and eigenvectors of the covariance matrix through the eigenvalue and eigenvector calculation formula; S22, sorting the eigenvalues ​​calculated in the step S21 in descending order, and taking the eigenvectors corresponding to the top four eigenvalues ​​as candidate contribution vectors of the performance impact factor, and determining the contribution vector of each performance impact factor based on the correlation between the performance impact factor and the base station parameter; S23, calculating the influence of the performance impact factor based on the product accumulation of the change vector of the base station parameter and the contribution vector of the performance impact factor.

[0011] In some embodiments of the present invention, the correlation between the performance influencing factor and the base station parameter is:

[0012] ;

[0013] ;

[0014] ;

[0015] in, Indicates the The correlation between performance influencing factors and base station parameters, Indicates the Contribution vector of performance influencing factors No. Elements of the row, Represents the standardized base station parameter sample matrix No. Elements of the column, Represents the candidate contribution vector of the performance influencing factor, Indicates the number of base station parameters, The value range is [1, 4]. In the contribution vector corresponding to the performance impact factor, the contribution corresponding to the base station parameters related to it is not 0, and the contribution corresponding to the base station parameters not related to it is 0.

[0016] In some embodiments of the present invention, the bandwidth is related to the cell downlink medium access control layer transmission rate, the number of downlink occupied physical resource blocks, the uplink signal to interference plus noise ratio, and the downlink medium access control layer transmission rate in the base station parameters; the delay is related to the cell downlink medium access control layer transmission rate, the number of downlink occupied physical resource blocks, and the downlink medium access control layer transmission rate in the base station parameters; the packet loss is related to the downlink data packet discard amount in the base station parameters; and the jitter is related to the modulation and coding strategy, the number of buffers occupied by the packet data convergence protocol layer, and the number of unused buffers in the packet data convergence protocol layer in the base station parameters.

[0017] In some embodiments of the present invention, in step S23, the influence degree of each performance influencing factor is calculated in the following manner:

[0018] ;

[0019] in, Indicates the The influence of each performance factor, Indicates the The change of base station parameters, Indicates the Contribution vector of performance influencing factors The The contribution of each base station parameter, Indicates the number of base station parameters.

[0020] In some embodiments of the present invention, the QoS parameters corresponding to bandwidth include: resource request priority, downlink guaranteed flow bit rate, downlink maximum flow bit rate and resource scheduling priority; the QoS parameters corresponding to packet loss include maximum packet loss rate; the QoS parameters corresponding to delay include: downlink guaranteed flow bit rate, downlink maximum flow bit rate and packet delay budget; the QoS parameters corresponding to jitter include: resource request priority and resource scheduling priority.

[0021] In some embodiments of the present invention, the preset rules are to adjust the QoS parameters corresponding to the performance impact factors with the highest impact, wherein: when the resource request priority and resource scheduling priority need to be adjusted, the QoS parameter adjustment strategy is to adjust the resource request priority and resource scheduling priority to a priority greater than or equal to the original highest priority + 1; when the downlink guaranteed flow bit rate and the downlink maximum flow bit rate need to be adjusted, the QoS parameter adjustment strategy is to adjust the flow bit rate to the sum of the original flow bit rate, the original flow bit rate and the product of the maximum impact; when the maximum packet loss rate needs to be adjusted, the QoS parameter adjustment strategy is to adjust the maximum packet loss rate to a packet loss rate less than or equal to the original packet loss rate of the mobile video service; when the packet delay budget needs to be adjusted, the QoS parameter adjustment strategy is to adjust the packet delay budget to the difference between the original packet delay budget, the original packet delay budget and the product of the maximum impact.

[0022] According to a second aspect of the present invention, a wireless communication system is provided, comprising: a wireless intelligent control platform, a wireless intelligent management platform, an application interface module, and a base station, wherein: the wireless intelligent management platform is used to train a mobile video freeze prediction model based on historical base station parameters and send the model to the wireless intelligent control platform; the wireless intelligent control platform is used to predict mobile video freeze based on base station parameters during online mobile video playback, using the mobile video freeze prediction model sent by the wireless intelligent management platform, and when mobile video freeze occurs, obtain a QoS parameter adjustment strategy using the method described in the first aspect of the present invention and send the strategy to the base station, and send a bit rate optimization strategy to the application interface module when the base station parameters are not adjustable; the base station is used to adjust parameters according to the QoS parameter adjustment strategy sent by the wireless intelligent control platform; and the application interface module is used to send the bit rate optimization strategy sent by the wireless intelligent control platform to a user terminal.

[0023] Compared with the existing technology, the advantages of the present invention are: a method for ensuring the smoothness of mobile video online playback provided by the present invention can judge the impact of different performance influencing factors on the degree of mobile video playback flow based on real-time base station parameters, thereby adopting targeted strategies to adjust QoS parameters. Compared with static template adjustment strategies, the solution of the present invention realizes reasonable and efficient allocation of resources and accurate, real-time, dynamic and sufficient guarantee of the smoothness of mobile video online playback, solving the current problems of low resource utilization rate of mobile video service smoothness QoS guarantee, insufficient smoothness requirement guarantee and unsatisfied user service quality requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The embodiments of the present invention are further described below with reference to the accompanying drawings, in which:

[0025] Figure 12. A flow chart of a method for ensuring smoothness of online mobile video playback according to an embodiment of the present invention;

[0026] Figure 2 Schematic diagram of the correlation between base station parameters, QoS parameters and performance impact factors according to an embodiment of the present invention;

[0027] Figure 3 Schematic diagram of the relationship between base station parameters, QoS parameters, impact and performance impact factors according to an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of a complete process of a method for ensuring smoothness of online mobile video playback according to an embodiment of the present invention;

[0029] Figure 5 A schematic diagram of a wireless communication system architecture according to an embodiment of the present invention;

[0030] Figure 6 A schematic diagram of a mobile video effect according to an embodiment of the present invention;

[0031] Figure 7 Schematic diagram of frame difference change of a moving video according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below through specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0033] As mentioned in the background technology, the current QoS guarantee for mobile video services mostly adopts the method of providing a dedicated QoS configuration template based on 5G QoS flow to ensure the QoS requirements of mobile video services. Although in most cases the QoS requirements can be met according to the matching QoS template, this guarantee method based on the 5G QoS flow to provide a dedicated QoS configuration template is relatively static. As long as the corresponding service is identified, the configured resources are allocated. Sometimes different mobile video users may have different QoS requirements. Some users do not need to allocate the configured resources to meet their needs, so that high-definition mobile videos can be played smoothly, while some users may still not be able to meet the QoS requirements after the resources are configured according to the QoS configuration template, causing the mobile video online playback to be stuck. It can be seen from this that the method of providing a dedicated QoS configuration template based on 5G QoS flow to ensure the QoS requirements of mobile video services cannot reasonably and efficiently utilize resources, nor can it fully guarantee the smoothness requirements of high-definition mobile video services. It is difficult to meet the real-time and accurate wireless resource requirements of mobile video and the service quality requirements of mobile video users.

[0034] In order to solve the above problems, the present invention provides a solution that can reasonably and efficiently utilize wireless network resources to fully ensure the smoothness of mobile video. Figure 1 As shown, the present invention provides a method for ensuring the smoothness of mobile video online playback, which includes: S1, obtaining base station parameters before the mobile video freezes and base station parameters when the mobile video freezes; S2, calculating the change vector of each base station parameter based on the obtained base station parameters, and calculating the impact of different performance impact factors on the smoothness of mobile video online playback based on the predetermined contribution vector of each base station parameter to the performance impact factor and the base station parameter change vector, wherein the performance impact factors include: bandwidth, packet loss, latency, and jitter; S3, obtaining a QoS parameter adjustment strategy according to preset rules; S4, when the base station is controllable, adjusting the parameters according to the QoS parameter adjustment strategy obtained in step S3; when the base station is not controllable, sending a bitrate optimization strategy to the mobile video user to ensure video smoothness. In the mobile video online playback smoothness assurance solution of the present invention, the impact of different performance impact factors on the mobile video playback flow is determined based on real-time base station parameters, and a targeted strategy is adopted to adjust the QoS parameters. Compared with static template adjustment strategies, the solution of the present invention can be adjusted dynamically in real time and meet the QoS requirements of different services.

[0035] In order to better understand the present invention, the solutions of the present invention are described in detail below with reference to specific embodiments.

[0036] In addition, before describing the embodiments of the present invention in detail, some of the terms used therein are explained as follows:

[0037] Principal component analysis is a statistical method for dimensionality reduction. It transforms the original random vector whose components are correlated into a new random vector whose components are uncorrelated through orthogonal transformation. This is expressed algebraically as transforming the covariance matrix of the original random vector into a diagonal matrix, and geometrically as transforming the original coordinate system into a new orthogonal coordinate system so that it points to the p orthogonal directions with the largest distribution of sample points. The multidimensional variable system is then subjected to dimensionality reduction processing so that it can be converted into a low-dimensional variable system with a higher accuracy. The low-dimensional system is then further converted into a one-dimensional system by constructing an appropriate value function.

[0038] 5G QoS parameters include 5G QoS Indicator (5QI), Allocation and Retention Priority (ARP), Guarantee Flow Bit Rate (GFBR), Maximum Flow Bit Rate (MFBR), Maximum Packet Loss Rate (MPLR), etc. Among them, ARP parameters include resource scheduling priority, preemption capability, and preemptibility. This priority defines the importance of the UE resource request. When system resources are limited, ARP parameters determine whether a new service QoS flow is accepted or rejected; GFBR is used to guarantee the upstream and downstream bandwidth values; MPLR indicates the maximum acceptable packet loss rate for a QoS flow.

[0039] 5QI is a scalar used to index a 5G QoS feature. Each 5G QoS feature can be given a 5QI identifier (5QI Value, used to identify the specific service type), resource type (Resource Type, specifying the type of service requested resources, including guaranteed bit rate (Guarantee Bite Rate, GBR) QoS flow, non-guaranteed bit rate (Non-GBR) QoS flow, and delay critical (GBR) QoS flow), default priority level (Default Priority Level, DPL, resource allocation priority), packet delay budget (Packet Delay Budget, PDB, the upper limit of the time delay between the UE and the N6 interface termination point in the UPF), packet error rate (Packet Error Rate, PER) and other parameters for a specified service. In the 5QI, a 5G QoS feature template is created for a specific service, and resource allocation is initialized based on the template to ensure the service quality of mobile services.

[0040] Then, the steps in the scheme of the present invention are described in detail.

[0041] 1. Step S1

[0042] In step S1, base station parameters before and during the mobile video freeze are obtained. According to one embodiment of the present invention, the base station parameters include: the cell downlink medium access control layer (MAC) transmission rate, the number of downlink occupied physical resource blocks, the uplink signal to interference plus noise ratio (SINR), the downlink MAC layer transmission rate, the modulation and coding strategy (modulation and coding scheme), the number of packet data convergence protocol (PDCP) layer occupied buffers, the number of unused PDCP layer buffers, and / or the number of downlink data packet discards. The subsequent embodiments will be described using these eight base station parameters as an example.

[0043] 2. Step S2

[0044] In step S2, the main purpose is to obtain the impact of different performance impact factors on the smoothness of mobile video online playback so as to subsequently specify targeted QoS parameter adjustment strategies. According to one embodiment of the present invention, the present invention uses principal component analysis to reduce the 8 base station parameters into 4 performance impact factors that affect the smoothness of mobile video online playback, and calculates the impact of each performance impact factor on the smoothness of mobile video online playback. According to one embodiment of the present invention, in step S2, the impact of different performance impact factors on the smoothness of mobile video online playback is calculated through steps S21-S23. Steps S21-S23 are described in detail below with reference to the accompanying drawings, embodiments and examples.

[0045] In step S21, a base station parameter sample matrix is ​​obtained, the sample matrix is ​​normalized to obtain the covariance matrix of the sample matrix, and the eigenvalues ​​and eigenvectors of the covariance matrix are calculated using the eigenvalue and eigenvector calculation formulas. (For ease of description, 、 、 、… To indicate 8 base station parameters, and Indicates the cell downlink MAC layer transmission rate, Indicates the number of physical resource blocks occupied by downlink, represents the uplink signal to interference plus noise ratio, Indicates the downlink MAC layer transmission rate, Indicates the modulation and coding strategy, Indicates the number of buffers occupied by the PDCP layer, Indicates the number of unused buffers in the PDCP layer. Indicates the number of discarded downlink data packets. 、 、 、 , respectively, to represent the impact of bandwidth, packet loss, delay, and jitter. Subsequent embodiments all adopt this representation method).

[0046] According to an example of the present invention, assuming that the base station parameter sample matrix is ​​1000 and the base station parameter sample matrix composed of 8 base station parameters has a dimension of 8*1000, the following steps are performed on the base station parameter sample matrix to obtain the eigenvalues ​​and eigenvectors of the covariance matrix:

[0047] First, the base station parameter sample matrix is ​​normalized, wherein the normalization method is:

[0048] ;

[0049] ;

[0050] ;

[0051] in, is the standardized base station parameter sample matrix element, is the original base station parameter sample matrix element, For the The mean of the base station parameters, For the first The standard deviation of the base station parameters, is the sample size. In the example of the present invention, .

[0052] The standardized base station parameter sample matrix is:

[0053] ;

[0054] in, is the number of base station parameters, is the sample size (in the present example, The value of is 8, The value of is 1000, and the number of base station parameters and sample size are only examples, and the present invention is not limited thereto).

[0055] Then, the covariance matrix of the standardized base station parameter sample matrix is ​​calculated based on the covariance matrix calculation formula, where the covariance matrix calculation method is:

[0056] ;

[0057] Thus, the covariance matrix of the standardized base station parameter sample matrix is ​​obtained as follows:

[0058] ;

[0059] in, It represents the covariance matrix Rank Elements of the column, It represents the normalized base station parameter sample matrix Rank Column elements, It represents the normalized base station parameter matrix the mean of the elements of the column, It represents the normalized base station parameter sample matrix Rank Column elements, It represents the normalized base station parameter sample matrix the mean of the elements of the column, , , (in the embodiment of the present invention ).

[0060] Secondly, calculate the eigenvalues ​​and eigenvectors of the covariance matrix, where the eigenvalues ​​and eigenvectors of the covariance matrix satisfy:

[0061] ;

[0062] in, is the eigenvalue, is a matrix of eigenvectors.

[0063] Based on the above formula, there are 8 eigenvalues ​​of the eigenvector that can be calculated (expressed as 、 、……、 ), and the eight eigenvectors corresponding to each eigenvalue (expressed as 、 、 、…、 )

[0064] Thus, the obtained feature vector can be expressed as:

[0065] ;

[0066] In step S22, the eigenvalues ​​calculated in step S21 are sorted in descending order, and the eigenvectors corresponding to the top four eigenvalues ​​are used as candidate contribution vectors of the performance impact factors (for example, assuming that based on the example in step S21, the eigenvalues ​​are sorted as follows: , then 、 、 、 The corresponding eigenvector 、 、 、 As a candidate contribution vector of the performance impact factor. ), and determine the contribution vector of each performance impact factor based on the correlation between the performance impact factor and the base station parameter. It should be noted that the correlation between the performance impact factor and the base station parameter indicates the corresponding relationship between the performance impact factor and the base station parameter. The correlation relationship between the performance impact factor and the base station parameter satisfies:

[0067] ;

[0068] ;

[0069] ;

[0070] in, Indicates the The correlation between performance influencing factors and base station parameters, Indicates the Contribution vector of performance influencing factors No. Elements of the row, Represents the standardized base station parameter sample matrix No. Elements of the column, Represents the candidate contribution vector of the performance influencing factor, Indicates the number of base station parameters, The value range is [1, 4]. In the contribution vector corresponding to the performance impact factor, the contribution corresponding to the base station parameters related to it is not 0, and the contribution corresponding to the base station parameters not related to it is 0.

[0071] According to one embodiment of the present invention, Figure 2 As shown, bandwidth is related to MAC transmission rate, physical resource block (PRB) utilization, and noise. Therefore, base station parameters related to bandwidth include: cell downlink MAC transmission rate, number of downlink occupied physical resource blocks, uplink signal to interference plus noise ratio, and downlink MAC transmission rate. Latency is related to MAC transmission rate and PRB utilization. Therefore, base station parameters related to latency include: cell downlink MAC transmission rate, number of downlink occupied physical resource blocks, and downlink MAC transmission rate. Packet loss is related to the number of packet losses. Therefore, base station parameters related to packet loss include: downlink data packet discard amount. Jitter is related to buffer occupancy and noise. Therefore, base station parameters related to jitter include: modulation and coding strategy, number of buffers occupied by the PDCP layer, and number of unused buffers at the PDCP layer.

[0072] Still referring to the example in step S21, assuming that the calculated eigenvalue The corresponding eigenvector , based on the correlation between base station parameters and performance influencing factors It can be seen that the base station parameters 、 、 、 The relevant performance factor is bandwidth, that is, That is the bandwidth contribution vector Similarly, based on the correlation between performance impact factors and base station parameters, the contribution vectors of packet loss, delay, and jitter can be obtained.

[0073] In step S23, the influence of the performance impact factor is calculated based on the product accumulation of the base station parameter change vector and the contribution vector of the performance impact factor. The influence of the performance impact factor is calculated as follows:

[0074] ;

[0075] in, Indicates the The influence of each performance factor, Indicates the The change of base station parameters, Indicates the Contribution vector of performance influencing factors The The contribution of each base station parameter, Indicates the number of base station parameters. It should be noted that due to The data is normalized, so the value range of each feature dimension is [0,1]. The value range of is [0,1], The value range of is [0,1], so The value range of is also [0,1].

[0076] Step S3

[0077] In the step S3, S3, a QoS parameter adjustment strategy is obtained according to a preset rule.

[0078] According to an embodiment of the present invention, the preset rule is to adjust the QoS parameter corresponding to the performance impact factor with the highest impact. Figure 2, we can see that the QoS parameters that can be adjusted include the following 6 types: resource request priority, downlink guaranteed flow bit rate (Downlink Guarantee Flow Bite Rate, DLGFBR), downlink maximum flow bit rate (Downlink Maximum Flow Bite Rate, DLMFBR), resource scheduling priority, maximum packet loss rate and packet delay budget; among them, the QoS parameters corresponding to bandwidth include: resource request priority, downlink guaranteed flow bit rate, downlink maximum flow bit rate and resource scheduling priority; the QoS parameters corresponding to delay include: downlink guaranteed flow bit rate, downlink maximum flow bit rate and packet delay budget; the QoS parameters corresponding to packet loss include: maximum packet loss rate; the QoS parameters corresponding to jitter include: resource request priority and resource scheduling priority. Based on the above correspondence, we can get the following: Figure 3 The corresponding relationship between the base station parameters, the influence of the performance impact factor, and the QoS parameters shown can be used to obtain the corresponding QoS parameters that need to be adjusted according to the performance impact factor. When the performance impact factor is bandwidth, the QoS parameters that need to be adjusted are resource request priority, downlink guaranteed flow bit rate, downlink maximum flow bit rate and resource scheduling priority; when the impact is When the performance impact factor is packet loss, the QoS parameter that needs to be adjusted is the maximum packet loss rate; when the impact is When the performance impact factor is delay, the QoS parameters that need to be adjusted are the downlink guaranteed flow bit rate, the downlink maximum flow bit rate and the packet delay budget; when the impact is When , the performance impact factor is jitter, and the QoS parameters that need to be adjusted are resource request priority and resource scheduling priority.

[0079] According to one embodiment of the present invention, when the QoS parameters that need to be adjusted are resource request priority and resource scheduling priority, the adjustment strategy needs to be determined based on the actual mobile video service access situation in the base station. If there are actually higher-priority and more urgent services in the base station, the priority of the mobile video service to be adjusted remains unchanged or is appropriately fine-tuned, and the adjusted priority cannot exceed other services with higher priorities; if there are no other more urgent service needs in the current base station, a list of service priorities in the current base station is obtained, and the priority index corresponding to the highest-priority service in the list is determined, and the resource request priority and resource scheduling priority of the mobile video service to be adjusted are adjusted to a priority greater than or equal to the original highest priority + 1.

[0080] According to one embodiment of the present invention, when the QoS parameters that need to be adjusted are the downlink guaranteed stream bit rate and the downlink maximum stream bit rate, the adjustment strategy is to adjust the stream bit rate to the sum of the original stream bit rate, the original stream bit rate and the product of the maximum impact. For example, the original stream bit rate is , the highest impact is , then the adjusted stream bitrate Expressed as: .

[0081] According to one embodiment of the present invention, when the QoS parameter that needs to be adjusted is the maximum packet loss rate, the adjustment strategy is to adjust the maximum packet loss rate to a packet loss rate that is less than or equal to the original packet loss rate of the mobile video service. For example, it is known that the maximum packet loss rate that can be allowed by the high-definition mobile video service is within 2%, so the packet loss rate of the mobile video service needs to be controlled within 2%. If the original value of the packet loss rate that needs to be adjusted is less than or equal to 2%, the packet loss rate needs to be adjusted with an adjustment scale of 0.1%; if the original value of the packet loss rate that needs to be adjusted is greater than 2%, the packet loss rate value is directly adjusted to 2%.

[0082] According to one embodiment of the present invention, when the QoS parameter to be adjusted is the packet delay budget, the adjustment strategy is to adjust the packet delay budget to the difference between the original packet delay budget, the original packet delay budget and the product of the maximum impact. For example, the original packet delay budget is , the highest impact is , then the adjusted packet delay budget Expressed as: .

[0083] Step S4

[0084] In step S4, when the base station is controllable, the parameters are adjusted according to the QoS parameter adjustment strategy obtained in step S3; when the base station is not controllable, a bit rate optimization strategy for ensuring video smoothness is sent to the mobile video user, wherein the bit rate optimization strategy indicates the recommended bit rate, and the user can adjust the video bit rate that causes the jamming to the recommended bit rate indicated in the bit rate optimization strategy to improve the jamming.

[0085] According to one embodiment of the present invention, when the resources in the base station can regulate QoS parameters according to the service quality requirements, the base station can directly adjust the corresponding QoS parameters based on the QoS parameter adjustment policy in step S3. When the base station carries a large number of services and the network load is relatively large, the base station cannot allocate more network resources to the mobile video service by directly adjusting the QoS parameters. In this case, the base station network status information can be shared with the mobile video service application end, and a bit rate optimization policy for ensuring video smoothness can be sent to the mobile video user based on the downlink guaranteed stream bit rate in the current QoS parameters. The mobile video server can adjust the bit rate of the mobile video according to the bit rate optimization policy so that the bit rate of the mobile video is less than the downlink guaranteed stream bit rate of the mobile video service QoS flow.

[0086] In order to better illustrate the implementation process of the mobile video online playback fluency guarantee method of the present invention, the following Figure 4 The whole process includes the following steps:

[0087] Step T1: Predicting freeze in the mobile video to determine whether freeze occurs in the mobile video. According to one embodiment of the present invention, freeze prediction is performed on the mobile video based on a mobile video freeze prediction model.

[0088] Step T2: When the mobile video freezes, obtain base station parameters before and during the mobile video freeze.

[0089] Step T3: Calculate the contribution vector of the performance impact factor based on the acquired base station parameters;

[0090] Step T4: Calculate the influence of the performance influencing factor based on the contribution vector of the performance influencing factor and the change vector of the base station parameter;

[0091] Step T5: Determine the maximum value of the influence of the calculated performance influence factor, and obtain the main performance influence factor affecting the smoothness of mobile video online playback based on the maximum value of the influence. Is it the maximum value? is the maximum value, it means that bandwidth is the main performance factor affecting the smoothness of mobile video online playback; if It is not the maximum value, judge again Is it the maximum value? is the maximum value, then packet loss is the main performance factor affecting the smoothness of mobile video online playback; if It is not the maximum value, judge again Is it the maximum value? is the maximum value, then the delay is the main performance factor affecting the smoothness of mobile video online playback; if If it is not the maximum value, is the maximum value. At this time, jitter is the main performance factor affecting the smoothness of mobile video online playback.

[0092] Step T6: Determine the QoS parameters and adjustment strategies that need to be adjusted based on the main performance influencing factors; when bandwidth is the main performance influencing factor, the QoS parameters that need to be adjusted are: resource request priority, downlink guaranteed flow bit rate, downlink maximum flow bit rate and resource scheduling priority; when packet loss is the main performance influencing factor, the QoS parameter that needs to be adjusted is the maximum packet loss rate; when delay is the main performance influencing factor, the QoS parameters that need to be adjusted are packet delay budget, downlink guaranteed flow bit rate and downlink maximum flow bit rate; when jitter is the main performance influencing factor, the QoS parameters that need to be adjusted are: resource request priority and resource scheduling priority.

[0093] The QoS parameter adjustment strategy is: when the QoS parameters that need to be adjusted are the resource request priority and the resource scheduling priority, the adjustment strategy is to adjust the resource request priority and the resource scheduling priority to a priority greater than or equal to the original highest priority + 1; when the QoS parameters that need to be adjusted are the downlink guaranteed flow bit rate and the downlink maximum flow bit rate, the QoS parameter adjustment strategy is to adjust the flow bit rate to the sum of the original flow bit rate, the original flow bit rate and the product of the maximum impact; when the QoS parameter that needs to be adjusted is the maximum packet loss rate, the QoS parameter adjustment strategy is to adjust the maximum packet loss rate to a packet loss rate less than or equal to the original packet loss rate of the mobile video service; when the QoS parameter that needs to be adjusted is the packet delay budget, the QoS parameter adjustment strategy is to adjust the packet delay budget to the difference between the original packet delay budget, the original packet delay budget and the product of the maximum impact.

[0094] Step T7: Determine whether the base station can be regulated according to the QoS parameter adjustment policy. If the base station is regulated, the corresponding QoS parameters are adjusted directly according to the QoS parameter adjustment policy; if the base station is not regulated, a bitrate recommendation is sent to the mobile video user terminal.

[0095] From the above, it can be seen that the method for ensuring the smoothness of mobile video online playback provided by the present invention can not only ensure the smoothness of mobile video online playback on the base station side, but also ensure the smoothness of mobile video online playback on the application side.

[0096] In order to better understand the implementation process of the method for ensuring the smoothness of mobile video online playback of the present invention, it is described below in conjunction with a wireless communication system.

[0097] like Figure 5 The structure shown is a wireless communication system for implementing the mobile video online playback fluency guarantee solution of the present invention, which includes: a wireless intelligent management platform, a wireless intelligent control platform, a base station and an application interface module.

[0098] The wireless intelligent management platform includes a policy management module, a mobile video freeze prediction model training module, a network operation and maintenance management module, and a resource orchestration management module. The policy management module is used to orchestrate and control interactions with the wireless intelligent control platform; the network operation and maintenance management module is used for network management, including fault management, configuration management, billing management, performance management, and security management; the resource orchestration management module is used to manage and coordinate network software components; and the mobile video freeze prediction model training module is used to train the mobile video freeze prediction model based on received historical data, mobile video demand, and historical mobile video playback freezes, and to send the trained model to the wireless intelligent control platform via the A1 interface.

[0099] The wireless intelligent control platform includes a data receiving module, a mobile video freeze prediction module, a mobile video fluency guarantee module, and a QoS parameter adjustment strategy delivery module. The data receiving module is used to receive the base station parameters acquired by the data acquisition module in the base station from the E2 interface; the mobile video freeze prediction module is used to predict freezes for the mobile video based on the mobile video freeze prediction model delivered by the wireless intelligent management platform; the mobile video fluency guarantee module is used to obtain a QoS parameter adjustment strategy based on the mobile video freeze prediction when the mobile video freezes, and to provide a bit rate optimization strategy to the application interface module when the base station cannot be controlled; the QoS strategy delivery module is used to deliver the QoS parameter adjustment strategy to the base station through the E2 interface;

[0100] The base station includes a data acquisition module and a QoS parameter adjustment strategy execution module. The data acquisition module is used to obtain base station parameters of mobile video; the QoS parameter adjustment strategy execution module is used to execute the QoS parameter adjustment strategy issued by the wireless intelligent control platform;

[0101] The application interface module is used to send the bit rate optimization strategy provided by the wireless intelligent control platform to the user end.

[0102] In order to further illustrate the effect of the present invention, the inventor used a method for ensuring the smoothness of mobile video online playback provided by the present invention to test mobile videos. By comparing the mobile video playback status before and after the mobile video smoothness guarantee is turned on, it is determined whether the mobile video smoothness guarantee is effective during the mobile video playback process. Figure 6As shown, the mobile video playback page can display the original mobile video, the change in mobile video frame difference, the operation button to enable mobile video smoothness guarantee, and the thumbnail of the video frame sampling during the online playback of mobile video. During the test, the sampling frequency is once every 1000ms. By comparing the current sampled video frame with the video frame at the previous sampling moment, the frame difference of the video at adjacent sampling time points is calculated to draw a line graph of the change in video frame difference. The video frame difference close to 0 indicates that there is no difference between the sampled video frames before and after, indicating that the video is stuck at this time; during the test, the mobile video frame difference change graph is as follows Figure 7 The first 60 seconds show the change of video frame difference when the mobile video online playback smoothness guarantee is not enabled, and the second 60 seconds show the change of video frame difference when the mobile video online playback smoothness guarantee is enabled. Figure 7 It can be seen that the low frame difference data in the last 60 seconds is less than that in the first 60 seconds, and the frequency of mobile video freezes is also lower than that in the first 60 seconds. According to the test results, it can be shown that the mobile video online playback smoothness guarantee method of the present invention can effectively guarantee the smoothness of mobile video online playback.

[0103] In summary, the method for ensuring the smoothness of mobile video online playback provided by the present invention can judge the impact of different performance influencing factors on the degree of mobile video playback flow based on real-time base station parameters, and thus adopt targeted strategies to adjust the QoS parameters. Compared with the static template adjustment strategy, the solution of the present invention realizes reasonable and efficient allocation of resources and accurate, real-time, dynamic and sufficient guarantee of the smoothness of mobile video online playback, solving the current problems of low resource utilization, insufficient guarantee of smoothness requirements and unmet user service quality requirements for mobile video service smoothness QoS guarantee.

[0104] It should be noted that although the above describes the various steps in a specific order, it does not mean that the steps must be performed in the above specific order. In fact, some of these steps can be executed concurrently or even in a different order as long as the required functions can be achieved.

[0105] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0106] A computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. Computer-readable storage media may include, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove having instructions stored thereon, and any suitable combination thereof.

[0107] While various embodiments of the present invention have been described above, the above descriptions are intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for ensuring the smoothness of online mobile video playback, characterized in that: The method includes responding to mobile video freeze in real time and performing the following steps: S1. Obtain base station parameters before the mobile video freezes and base station parameters when the mobile video freezes, the base station parameters including: cell downlink medium access control layer transmission rate, number of downlink occupied physical resource blocks, uplink signal to interference plus noise ratio, downlink medium access control layer transmission rate, modulation and coding strategy, number of occupied buffers of packet data convergence protocol layer, number of unused buffers of packet data convergence protocol layer, and / or number of discarded downlink data packets; S2. Calculating a change vector of each base station parameter based on the acquired base station parameters, and calculating the impact of different performance impact factors on the smoothness of mobile video online playback based on a predetermined contribution vector of each base station parameter to a performance impact factor and the change vector of the base station parameter, wherein the performance impact factors include bandwidth, packet loss, latency, and jitter; S3. Obtain QoS parameter adjustment strategy according to preset rules. QoS parameters corresponding to bandwidth include: resource request priority, downlink guaranteed flow bit rate, downlink maximum flow bit rate and resource scheduling priority; QoS parameters corresponding to packet loss include maximum packet loss rate; QoS parameters corresponding to delay include: downlink guaranteed flow bit rate, downlink maximum flow bit rate and packet delay budget; QoS parameters corresponding to jitter include: resource request priority and resource scheduling priority; the preset rules are to adjust the QoS parameters corresponding to the performance influencing factors with the highest impact, wherein: when it is necessary to adjust the resource request priority and resource scheduling priority, the QoS parameters The adjustment strategy is to adjust the resource request priority and resource scheduling priority to a priority greater than or equal to the original highest priority + 1; when the downlink guaranteed flow bit rate and the downlink maximum flow bit rate need to be adjusted, the QoS parameter adjustment strategy is to adjust the flow bit rate to the sum of the original flow bit rate, the original flow bit rate and the product of the maximum impact; when the maximum packet loss rate needs to be adjusted, the QoS parameter adjustment strategy is to adjust the maximum packet loss rate to a packet loss rate less than or equal to the original packet loss rate of the mobile video service; when the packet delay budget needs to be adjusted, the QoS parameter adjustment strategy is to adjust the packet delay budget to the difference between the original packet delay budget, the original packet delay budget and the product of the maximum impact; S4. When the base station is controllable, adjust the parameters according to the QoS parameter adjustment strategy obtained in step S3; when the base station is not controllable, send a bit rate optimization strategy to the mobile video user to ensure video fluency.

2. The method according to claim 1, characterized in that The step S2 comprises: S21. Obtain a base station parameter sample matrix, standardize the sample matrix to obtain a covariance matrix of the sample matrix, and calculate the eigenvalues ​​and eigenvectors of the covariance matrix using eigenvalue and eigenvector calculation formulas; S22. Sort the eigenvalues ​​calculated in step S21 in descending order, use the eigenvectors corresponding to the top four eigenvalues ​​as candidate contribution vectors of the performance impact factors, and determine the contribution vector of each performance impact factor based on the correlation between the performance impact factors and the base station parameters; S23. Obtain the influence of the performance impact factor based on the product accumulation calculation of the base station parameter change vector and the contribution vector of the performance impact factor.

3. The method according to claim 2, characterized in that The correlation between the performance impact factor and the base station parameters is: ； ； ； in, Indicates the The correlation between performance influencing factors and base station parameters, Indicates the Contribution vector of performance influencing factors No. Elements of the row, Represents the standardized base station parameter sample matrix No. Elements of the column, Represents the candidate contribution vector of the performance influencing factor, Indicates the number of base station parameters, The value range is [1,4]; In the contribution vector corresponding to the performance impact factor, the contribution corresponding to the base station parameter related thereto is not 0, and the contribution corresponding to the base station parameter not related thereto is 0.

4. The method according to claim 3, characterized in that The bandwidth is related to the cell downlink medium access control layer transmission rate, the number of physical resource blocks occupied by downlink, the uplink signal to interference plus noise ratio, and the downlink medium access control layer transmission rate in the base station parameters; The delay is related to the cell downlink medium access control layer transmission rate, the number of downlink occupied physical resource blocks, and the downlink medium access control layer transmission rate in the base station parameters; Packet loss is related to the amount of downlink data packets dropped in the base station parameters; Jitter is related to the modulation and coding strategy in the base station parameters, the number of buffers occupied by the packet data convergence protocol layer, and the number of unused buffers in the packet data convergence protocol layer.

5. The method according to claim 2, characterized in that In step S23, the influence degree of each performance influencing factor is calculated in the following manner: ； in, Indicates the The influence of each performance factor, Indicates the The change of base station parameters, Indicates the Contribution vector of performance influencing factors The The contribution of each base station parameter, Indicates the number of base station parameters.

6. A wireless communication system, characterized in that: The system includes: a wireless intelligent control platform, a wireless intelligent management platform, an application interface module and a base station, wherein: The wireless intelligent management platform is used to train a mobile video freeze prediction model based on historical base station parameters and send it to the wireless intelligent control platform; The wireless intelligent control platform is used to predict mobile video freezes based on base station parameters during online mobile video playback using a mobile video freeze prediction model issued by the wireless intelligent management platform, and when mobile video freezes, obtain a QoS parameter adjustment strategy using the method described in any one of claims 1 to 5 and issue it to the base station, and send a bit rate optimization strategy to the application interface module when the base station parameters are not adjustable; The base station is used to adjust parameters according to the QoS parameter adjustment policy issued by the wireless intelligent control platform; The application interface module is used to send the bit rate optimization strategy issued by the wireless intelligent control platform to the user end.

7. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program can be executed by a processor to implement the steps of any one of the methods of claims 1 to 5.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the steps of the method according to any one of claims 1 to 5.

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