A method for low-latency video transmission under 5G network

Abstract

This invention relates to the field of video transmission technology, specifically a low-latency video transmission method under a 5G network, comprising: S1: acquiring 5G network speed data and predicting the network speed at the next moment based on the acquired speed data; S2: comparing the predicted network speed at the next moment with a preset threshold; if the predicted network speed at the next moment is less than the preset threshold, splitting the video frame data packets to be transmitted at the next moment and prioritizing them according to the video frame type; S3: configuring the video frame data to be transmitted at the predicted network speed according to the priority order of the video frame type. The low-latency video transmission method under a 5G network provided by this invention can predict potential changes in 5G network speed in advance, adjust the size of the video data before a sudden drop in 5G network transmission speed, avoid video stuttering and latency caused by insufficient instantaneous network speed, and ensure smooth video playback.

Description

Technical Field

[0001] This invention relates to the field of video transmission technology, specifically a low-latency video transmission method under a 5G network. Background Technology

[0002] Fifth-generation mobile communication (5G) is the latest generation of cellular mobile communication technology, and an extension of 4G and 3G technologies. 5G is a new generation of broadband mobile communication technology characterized by high speed, low latency, and massive connectivity.

[0003] With the widespread adoption of 5G applications, numerous application scenarios have emerged, such as in industry, video conferencing, and live streaming. In some scenarios, the real-time performance of video transmission is critical, requiring high average and instantaneous network speeds to prevent stuttering during playback. While 5G networks offer high average speeds, they cannot completely eliminate video stuttering issues caused by insufficient instantaneous network speeds. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a low-latency video transmission method under 5G networks to avoid video stuttering caused by insufficient instantaneous network speed.

[0005] This invention is achieved through the following technical solution:

[0006] A method for low-latency video transmission under a 5G network includes the following steps:

[0007] S1: Obtain 5G network speed data and predict the network speed at the next moment based on the obtained network speed data;

[0008] S2: Compare the predicted network speed at the next moment with the preset threshold. If the predicted network speed at the next moment is less than the preset threshold, the video frame data packets to be transmitted at the next moment will be split and sorted according to the priority of the video frame type.

[0009] S3: Based on the predicted network speed at the next moment, configure the video frame data to be transmitted at that predicted network speed according to the priority order of video frame types.

[0010] Furthermore, S1 predicts the network speed at the next moment based on the acquired network speed data, specifically including the following steps:

[0011] Based on the acquired 5G network speed data, a linear model of the 5G network speed is constructed. The network speed value at the next moment is calculated based on the slope of the linear model at the current moment, thus obtaining the predicted value of the network speed at the next moment.

[0012] Furthermore, the network speed at the next moment is calculated based on the slope of the linear model at the current moment, as shown in the following expression:

[0013] V(t+1)=V'(t)·△t+V(t)

[0014] Where V(t+1) is the predicted network speed at the next moment; V'(t) is the slope of the linear model at the current moment; Δt is the time difference between the next moment and the current moment; and V(t) is the network speed at the current moment.

[0015] Furthermore, in S2, the video frame data packets to be transmitted in the next moment are split and prioritized according to the video frame type. The specific steps are as follows:

[0016] The video frame data packets are split into I-frames, P-frames, and B-frames, where I-frames have a higher priority than P-frames, and P-frames have a higher priority than B-frames.

[0017] Furthermore, the I-frame grid is divided into multiple sub-units, each of which contains image matrix data;

[0018] Sub-units are divided into critical sub-units and non-critical sub-units according to a preset division method; among them, critical sub-units have higher priority than non-critical sub-units.

[0019] Preferably, the sub-units are divided into critical sub-units and non-critical sub-units according to a preset division method. The specific steps are as follows:

[0020] Locate the center region [x, y] of the image. The rectangular region formed by the coordinates of the upper left point [xa, ya] and the lower right point [x+a, y+a] is the key region. Sub-units falling within the key region are key sub-units, and sub-units falling outside the key region are non-key sub-units.

[0021] Preferably, a set number of consecutive video frames are used as input, the scaling ratio of the image in the key area at the next moment is predicted based on the scaling trend of the image in the key area in the consecutive video frames, and the rectangular key area in the image at the next moment is scaled accordingly based on the predicted scaling ratio.

[0022] There are several ways to predict the scaling ratio of the image in the key region at the next moment based on the scaling trend of the image in the key region in consecutive video frames, such as:

[0023] 1) Calculate the scaling ratio of the image in the key region in two adjacent video frames, and take the average of the scaling ratios. Use this average as the predicted value of the scaling ratio of the image in the key region at the next moment.

[0024] 2) Calculate the scaling ratio of the image in the key region in two adjacent video frames, construct a linear model based on the obtained scaling ratio, and calculate the scaling ratio of the image in the key region in the next moment based on the slope of the linear model at the current moment.

[0025] Preferably, a preset number of consecutive video frames are used as input, specifically: the 2-5 consecutive video frames adjacent to the current video frame are used as input.

[0026] Furthermore, the video frame data transmitted at the predicted network speed is configured in S3 as follows:

[0027] Configure the maximum amount of data that can be transmitted under the predicted network speed; the remaining data in this frame will not be transmitted.

[0028] Given the same hardware foundation, the method of the present invention has a certain degree of compatibility. Therefore, the present invention also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the above-mentioned low-latency video transmission method under 5G network.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The low-latency video transmission method under 5G network provided by this invention can predict possible changes in 5G network speed in advance and adjust the size of video data before the instantaneous transmission speed of 5G network drops, so as to avoid video stuttering and delay caused by insufficient instantaneous network speed and ensure smooth video playback. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a simplified flowchart of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0034] See attached document Figure 1 As shown, a low-latency video transmission method under a 5G network includes the following steps:

[0035] S1: Obtain 5G network speed data and predict the network speed at the next moment based on the obtained speed data; the specific steps are as follows:

[0036] Based on the acquired 5G network speed data, a linear model of the 5G network speed is constructed. The network speed value for the next moment is calculated based on the slope of the linear model at the current moment, thus obtaining the predicted network speed for the next moment. The expression is as follows:

[0037] V(t+1)=V'(t)·△t+V(t)

[0038] Where V(t+1) is the predicted network speed at the next moment; V'(t) is the slope of the linear model at the current moment; Δt is the time difference between the next moment and the current moment; and V(t) is the network speed at the current moment. This invention effectively avoids video buffering by predicting the network speed at the next moment and adjusting the size of the video data to be transmitted in a timely manner.

[0039] S2: Compare the predicted network speed for the next moment with a preset threshold. If the predicted network speed for the next moment is less than the preset threshold, then split the video frame data packets to be transmitted in the next moment and prioritize them according to the video frame type. The specific steps are as follows:

[0040] Video frame data packets are split into I-frames, P-frames, and B-frames, with I-frames having higher priority than P-frames, and P-frames having higher priority than B-frames. The I-frames, P-frames, and B-frames are ordered according to their degree of influence. Since B-frames only affect themselves and have the least impact on I-frames and P-frames, they have the lowest priority. P-frames are prediction frames, and I-frames, being key frames, have the greatest impact on B-frames and P-frames, thus having the highest priority. This invention sorts video frames according to their degree of influence. When the network speed is slow, below a preset threshold, video data is transmitted according to priority. This method reduces the size of the transmitted data, thereby avoiding video stuttering caused by insufficient instantaneous network speed.

[0041] In this embodiment, the I-frame grid is further divided into multiple sub-units, each containing image matrix data. Then, according to a preset division method, the sub-units are divided into critical sub-units and non-critical sub-units, with critical sub-units having a higher priority than non-critical sub-units. That is, this invention further divides the critical regions within the I-frame. During transmission, the critical sub-units of the I-frame are transmitted first. This method can further reduce the size of the video data without affecting the user's acquisition of images of the critical regions, thereby achieving smooth video playback even when instantaneous network speed is insufficient.

[0042] When recording live video streams and video conferences, the key information to be conveyed to the user is mainly presented in the central area of ​​the frame. Therefore, the specific steps for dividing key and non-key sub-units in this embodiment are as follows:

[0043] The central region [x, y] of the image is located. A rectangular area formed by the coordinates of the top-left point [xa, ya] and the bottom-right point [x+a, y+a] is designated as the critical region. Sub-units falling within this critical region are considered critical sub-units, while those outside are considered non-critical sub-units. During transmission, critical sub-units falling within the central rectangular region (critical region) of the I-frame are transmitted first, thereby further reducing the video data size without affecting the transmission of the main information.

[0044] As a further optimization and improvement, this embodiment takes the previous 2-5 consecutive video frames adjacent to the current video frame as input, predicts the scaling ratio of the image in the key area at the next moment based on the scaling trend of the image in the key area in the consecutive video frames, and scales the rectangular key area in the image at the next moment accordingly based on the predicted scaling ratio.

[0045] In live video streaming and video conferencing, the subject is primarily displayed in a rectangular area in the center of the frame (this area is the critical region). However, as the subject moves closer to or further from the camera, its size within the critical region changes accordingly, meaning the area of ​​the image presented by the subject in the critical region changes. This solution detects the scaling trend of the image area presented by the subject in the critical region within the previous 2-5 frames of video, predicts the scaling ratio of the image area presented by the subject in the critical region at the next moment, and then synchronously adjusts the size of the critical region based on this predicted scaling ratio. By adjusting the size of the critical region accordingly as the subject moves closer to or further from the camera, the identification of the critical region in the image becomes more accurate.

[0046] There are several ways to predict the scaling ratio of the image in the key region at the next moment based on the scaling trend of the image in the key region in consecutive video frames, such as:

[0047] 1) Calculate the scaling ratio of the image in the key region in two adjacent video frames, and take the average of the scaling ratios. Use this average as the predicted value of the scaling ratio of the image in the key region at the next moment.

[0048] 2) Calculate the scaling ratio of the image in the key region in two adjacent video frames, construct a linear model based on the obtained scaling ratio, and calculate the scaling ratio of the image in the key region in the next moment based on the slope of the linear model at the current moment (the prediction method of this scaling ratio is similar to the network speed prediction method).

[0049] S3: Based on the predicted network speed at the next moment, configure the video frame data to be transmitted at that predicted network speed according to the priority order of video frame types. Specifically: Based on the predicted network speed and according to the priority order, configure the maximum amount of data that can be transmitted at that predicted network speed. The remaining data in the frame will not be transmitted, thereby reducing the size of the video data and avoiding video stuttering caused by insufficient instantaneous network speed under 5G network.

[0050] The low-latency video transmission method under 5G network provided by this invention can predict possible changes in 5G network speed in advance and adjust the size of video data before the instantaneous transmission speed of 5G network drops, avoiding video stuttering and delay caused by insufficient instantaneous network speed, ensuring smooth video playback, and is suitable for further promotion and application.

[0051] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0052] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A method for low-latency video transmission under a 5G network, characterized in that, Includes the following steps: S1: Obtain 5G network speed data and predict the network speed at the next moment based on the obtained speed data; specifically including the following steps: Based on the acquired 5G network speed data, a linear model of the 5G network speed is constructed. The network speed value for the next moment is calculated based on the slope of the linear model at the current moment, thus obtaining the predicted value of the network speed for the next moment. The expression is as follows: V(t+1)=V'(t)·△t+V(t) Where V(t+1) is the predicted network speed at the next moment; V'(t) is the slope of the linear model at the current moment; Δt is the time difference between the next moment and the current moment; and V(t) is the network speed at the current moment. S2: Compare the predicted network speed at the next moment with a preset threshold. If the predicted network speed at the next moment is less than the preset threshold, then the video frame data packets to be transmitted at the next moment are split and prioritized according to the video frame type. The specific steps for splitting and sorting the video frame data packets are as follows: The video frame data packet is split into I-frames, P-frames and B-frames, where I-frames have a higher priority than P-frames, and P-frames have a higher priority than B-frames. The I-frame grid is divided into multiple sub-units, each containing image matrix data. According to a preset division method, the sub-units are further divided into critical sub-units and non-critical sub-units, with critical sub-units having higher priority than non-critical sub-units. The division method for critical and non-critical sub-units is as follows: Locate the center region point [x, y] of the image. The rectangular region formed by the coordinates of the upper left point [xa, ya] and the coordinates of the lower right point [x+a, y+a] is the key region. Sub-units falling within the key region are key sub-units, and sub-units falling outside the key region are non-key sub-units. Using a preset number of consecutive video frames as input, the scaling ratio of the image in the key region at the next moment is predicted based on the scaling trend of the image in the key region in the consecutive video frames. Specifically, the scaling ratio of the image in the key region in two adjacent video frames is calculated, a linear model is constructed based on the obtained scaling ratio, and the scaling ratio of the image in the key region at the next moment is calculated based on the slope of the linear model at the current moment. Next, the rectangular key region in the image at the next time step is scaled accordingly using the predicted scaling ratio; S3: Based on the predicted network speed at the next moment, configure the video frame data to be transmitted at the predicted network speed according to the priority order of video frame types; During transmission, key sub-units within the rectangular area in the center of the image in the I-frame are transmitted first, thereby further reducing the size of the video data without affecting the transmission of the main information.

2. The low-latency video transmission method under a 5G network according to claim 1, characterized in that, The input consists of a preset number of consecutive video frames, specifically the 2-5 consecutive video frames adjacent to the current video frame.

3. The low-latency video transmission method under a 5G network according to claim 1, characterized in that, In S3, the video frame data to be transmitted under the predicted network speed is configured as follows: Configure the maximum amount of data that can be transmitted under the predicted network speed; the remaining data in this frame will not be transmitted.

4. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the low-latency video transmission method under a 5G network as described in any one of claims 1 to 3.

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