Low-delay video transmission method and system suitable for weak signal scene

Abstract

The invention relates to the technical field of remote video transmission, in particular to a low-delay video transmission method and system suitable for a weak signal scene. The method comprises the following steps that a video capturing module is arranged at a robot end and used for capturing video data in real time; a video coding module is arranged at the robot end, and AV1 coding is adopted to code captured video data; a video transmission module is arranged at the robot end, and coded video data are sent to the control station end through P2P tunnel communication; a video receiving module is arranged at the control station and used for receiving video data sent through P2P tunnel communication; a video decoding module is arranged at a control station end and is used for decoding the received video data so as to recover the original video data; and a video display module is arranged at the control station end and is used for displaying the decoded video data. According to the invention, the coding algorithm and the transmission protocol are optimized, so that fast, efficient and low-delay video transmission is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote video transmission, and more specifically, relates to a low-latency video transmission method and system suitable for weak signal scenarios. Background Art

[0002] In modern society, with the rapid development of information technology and the widespread application of intelligent robot technology, remote-controlled robots have become an important tool in many industries, medical care, security, education and other fields. Remote-controlled robots usually require real-time, stable, and low-latency video transmission so that the operator can accurately obtain the on-site situation and make corresponding decisions. However, in practical applications, especially in remote areas, harsh environments or complex network conditions, how to achieve high-quality, low-latency video transmission has become one of the technical difficulties due to problems such as insufficient signal strength, high network latency, and high packet loss rate.

[0003] Existing video transmission technologies mainly rely on traditional video coding standards such as H.264 and H.265, which can provide good video quality and low latency under good network conditions. However, in weak signal scenarios, the performance of these standards is often unsatisfactory.

[0004] H.264 / AVC (Advanced Video Coding): H.264 is a widely used video coding standard with high compression efficiency and good video quality. However, H.264 performs poorly in weak signal environments, mainly because its entropy coding part relies on context-adaptive binary arithmetic coding (CABAC), which has poor adaptability to changes in network conditions. When the network bandwidth is unstable or the signal strength is weak, H.264-encoded video data is prone to delay and packet loss, affecting the real-time and smoothness of the video.

[0005] H.265 / HEVC (High Efficiency Video Coding): H.265 is the successor standard of H.264, which further improves the compression efficiency of videos. However, H.265 also faces the problem of adaptability in weak signal scenarios. Although its entropy coding part adopts an improved version of context-adaptive binary arithmetic coding (CABAC), the performance of H.265 is still limited in environments with high packet loss rate and high latency, especially in real-time transmission, where the latency problem is particularly prominent.

[0006] VP9: VP9 is a video coding standard developed by Google, which has high compression efficiency and good video quality. VP9 performs relatively well in weak signal environments, but there are still some problems. Although its coding algorithm has increased network adaptability to a certain extent, it still requires a larger bandwidth and a lower packet loss rate to ensure real-time transmission.

[0007] IEEE 802.11ac / 802.11ax: These are Wi-Fi standards for transmitting video data within a local area network. Although 802.11ax (Wi-Fi 6) has significantly improved bandwidth and anti-interference capabilities compared to 802.11ac (Wi-Fi 5), signal strength is still easily affected in long-distance transmission or complex environments, resulting in increased latency and packet loss in video transmission.

[0008] Cellular network transmission: Although cellular networks such as 4G and 5G have improved in coverage and transmission speed, the signal strength is still limited in certain environments such as remote areas, mountainous areas or basements. In addition, the bandwidth and stability of cellular networks are also affected by network congestion and multi-user competition, making it difficult to ensure high-quality video transmission.

[0009] Deficiencies of existing technology:

[0010] (1) Poor network adaptability: The existing video coding standards have poor adaptability in weak signal environments, which is mainly manifested in high latency and high packet loss rate. This problem not only affects the real-time and smoothness of the video, but may also cause the operator to be unable to obtain accurate on-site conditions in a timely manner when remotely controlling the robot, thereby affecting the accuracy and safety of the operation.

[0011] (2) Lack of dynamic adjustment mechanism: Existing video encoding technologies usually use fixed or semi-fixed encoding parameters, which cannot be dynamically adjusted according to real-time network conditions and video content complexity. This makes it difficult to maintain consistent video transmission quality when bandwidth is unstable, and sometimes the image quality suddenly drops or the delay increases.

[0012] (3) Low data transmission reliability: In a weak signal environment, packet loss is prone to occur during data transmission. Although existing transmission technologies such as TCP / IP have a certain retransmission mechanism, in an environment with a high packet loss rate, too many retransmissions will further increase the delay. In addition, the retransmission mechanism may fail in extreme cases, resulting in unreliable data transmission.

[0013] (4) Weak path selection and switching capabilities: Existing data transmission technologies are weak in path selection and switching capabilities. In a complex network environment, transmission on a single path may be affected by congestion or interruption, resulting in discontinuous and unstable video streams. Although existing multi-path transmission technologies can provide backup paths, they suffer from delays and jitters during path switching.

[0014] (5) Insufficient security: Existing technologies are insufficient in terms of data transmission security. Although some transmission technologies use basic encryption mechanisms, these encryption mechanisms are limited in effectiveness in the face of high-intensity network attacks or data theft. Especially in scenarios where sensitive data needs to be transmitted, data security is crucial. Summary of the invention

[0015] In order to solve the deficiencies in the prior art, the present invention provides a low-latency video transmission method and system suitable for weak signal scenarios, which realizes fast, efficient and low-latency video transmission by optimizing the encoding algorithm and transmission protocol.

[0016] The present invention adopts the following technical solution.

[0017] A first aspect of the present invention provides a low-delay video transmission method suitable for a weak signal scenario, comprising the following steps:

[0018] The acquisition end captures the environmental video, converts it into a digital signal, and performs preprocessing;

[0019] According to the network conditions and the complexity of the video content, the pre-processed video data is encoded using AV1 encoding with dynamically adjusted encoding parameters to reduce encoding delay;

[0020] The acquisition end sends the encoded video data to the receiving end through P2P tunnel communication;

[0021] The receiving end receives the video data sent through the P2P tunnel communication, implements data packet reorganization, and automatically reorganizes and sorts discontinuous or misordered data packets to ensure the integrity and consistency of the video data before decoding;

[0022] The receiving end decodes the received video data using AV1 decoding to restore the original video data;

[0023] Display the decoded video data at the receiving end.

[0024] Preferably, dynamically adjusting AV1 encoding parameters includes:

[0025] Real-time network monitoring: obtain the current network status in real time, including bandwidth, delay, and packet loss rate;

[0026] Video content analysis: Real-time analysis of the complexity of video content, including: degree of motion, brightness changes;

[0027] Encoding parameter adjustment: custom dynamically adjust the encoding bit rate, frame rate and resolution;

[0028] Ensure that the delay and packet loss rate of video transmission reach the optimal balance when the network bandwidth is unstable.

[0029] Preferably, the acquisition end sends the encoded video data to the receiving end through P2P tunnel communication, including:

[0030] The FEC mechanism is adopted to add redundant information during the data transmission process. If some data packets are lost in a weak signal environment, the video data is restored based on the received data packets and redundant information, reducing video transmission errors and retransmission times.

[0031] Preferably, the P2P tunnel deployed in the cloud uses multi-path transmission. If the main path is congested or interrupted, it automatically switches to the backup path to continue transmission. Multi-path transmission includes:

[0032] Path monitoring: The path monitoring module monitors the status of each path in real time, including bandwidth, delay, and packet loss rate;

[0033] Intelligent path selection algorithm: select the optimal path for data transmission based on the path conditions;

[0034] Path switching strategy: During the path switching process, a smooth switching strategy is adopted to reduce the delay and jitter during switching.

[0035] Preferably, the receiving end can send quality feedback information to the collecting end according to the decoded video quality, and the sending end can adjust the transmission strategy accordingly, including increasing FEC redundancy and reducing video encoding parameters, so as to achieve end-to-end video quality optimization.

[0036] Preferably, the receiving end adopts a low-delay decoding buffer algorithm to predict the arrival time of the video frame and dynamically adjust the decoding buffer size.

[0037] Preferably, the acquisition end includes multiple cameras, and the multiple cameras work together, including:

[0038] Multi-camera collaborative control: Multiple cameras on the acquisition end work together to capture video data from multiple angles at the same time, and ensure the consistency of video capture through synchronization signals;

[0039] Video stream merging: The acquisition end merges multiple video streams into a single video stream for transmission;

[0040] Video stream separation: The receiving end separates and restores multiple channels of video.

[0041] Preferably, the acquisition end uses AES-256 encryption algorithm to encrypt the video data before sending the data.

[0042] A second aspect of the present invention provides a low-delay video transmission system suitable for weak signal scenarios, and runs a low-delay video transmission method suitable for weak signal scenarios:

[0043] The acquisition end includes: video capture module, video encoding module, video transmission module; the receiving end includes: video receiving module, video decoding module, video display module;

[0044] The video capture module is used to capture environmental video, convert it into digital signals, and perform preprocessing;

[0045] The video encoding module encodes the preprocessed video data using AV1 encoding and dynamically adjusts AV1 encoding parameters;

[0046] The video transmission module is used to send the encoded video data to the receiving end through P2P tunnel communication;

[0047] The video receiving module is used to receive video data sent through P2P tunnel communication, implement data packet reorganization, and automatically reorganize and sort discontinuous or misordered data packets to ensure the integrity and consistency of the video data before decoding;

[0048] The video decoding module is used to decode the received video data using AV1 decoding and dynamically adjust the decoding buffer size to restore the original video data;

[0049] The video display module is used to use a set graphics library to achieve low-latency display and display the decoded video data.

[0050] Preferably, the video capture module includes a plurality of cameras working in coordination;

[0051] Multiple cameras working together capture video data from multiple angles, and the video encoding module merges multiple video streams into a single video stream for transmission. The video decoding module at the receiving end separates and restores multiple videos.

[0052] Compared with the prior art, the beneficial effects of the present invention include at least: through a series of technical improvements, not only the efficiency and quality of video transmission in weak signal scenarios are improved, but also the stability and security of the system are enhanced, and strong technical support is provided for applications such as remote control robots, as follows:

[0053] (1) Improving network adaptability: The present invention adopts AV1 encoding technology and an algorithm for dynamically adjusting encoding parameters, which can effectively cope with network changes in weak signal environments, significantly reduce the delay and packet loss rate of video transmission, and thus improve the real-time and smoothness of the video.

[0054] (2) Enhanced data transmission reliability: The present invention combines the forward error correction (FEC) mechanism with multipath transmission technology to ensure the complete transmission of video data even in an environment with a high packet loss rate, reduce the delay caused by retransmission, and ensure the reliability of data transmission.

[0055] (3) Optimizing path selection and switching capabilities: The application of the intelligent path selection algorithm and multi-path transmission technology of the present invention enables the present invention to automatically select the best path in a complex network environment and maintain the continuity and stability of the video stream when switching paths.

[0056] (4) Improved security: The present invention uses the AES-256 encryption algorithm to encrypt video data, which effectively improves the security of data transmission and prevents sensitive data from being illegally intercepted or tampered with during transmission.

[0057] (5) Support for multi-angle observation: The present invention can provide multi-angle video data through multi-camera collaboration and video stream merging technology to meet the needs of multi-angle observation in specific application scenarios.

[0058] (6) Low-latency decoding and playback: The application of the low-latency decoding buffer algorithm of the present invention enables the present invention to effectively reduce visual delays during the decoding and playback process, providing a smoother viewing experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flowchart of a low-delay video transmission technology suitable for weak signal scenarios in the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, ordinary technicians in this field can make the above-mentioned invention without creative work.

[0061] The present invention provides a low-delay video transmission technology suitable for weak signal scenarios, aiming to solve the problems of high delay, high packet loss rate, poor network adaptability, low data transmission reliability, weak path selection and switching capabilities, and insufficient security in the existing technology of video transmission in weak signal environments. The following introduces the implementation details and working principles of each module.

[0062] like Figure 1 As shown, embodiment 1 of the present invention provides a low-delay video transmission method suitable for weak signal scenarios, comprising the following steps:

[0063] Step 1: Capture the environment video, convert it into a digital signal, and perform preprocessing. At the acquisition end, for example but not limited to, a video capture module is set at the robot end to capture video data in real time, wherein the video capture module mainly includes a camera.

[0064] Preferably but not restrictively, a high-resolution camera (1080p 60fps) on the robot side collects optical signals in real time to capture environmental video to ensure that the captured video data is of high quality; an image sensor quickly converts the optical signal into a digital signal through high-speed image capture; and an image processing unit performs preliminary processing on the captured video data, such as noise reduction, color correction, etc., to improve the video quality.

[0065] Step 2: According to the real-time network status and the complexity of the video content, the pre-processed video data is encoded using AV1 encoding with dynamically adjusted encoding parameters. For example, but not limited to, a video encoding module is set on the robot side, and the captured video data is encoded using AV1 encoding, wherein AV1 encoding uses a multi-symbol context adaptive arithmetic coding method based on CDF (Cumulative Distribution Function) to improve the entropy coding throughput and reduce the coding delay; according to the network status feedback from the real-time network monitoring module and the video complexity analyzed by the video content analysis module, the encoding parameters are dynamically adjusted to ensure that the delay and packet loss rate of video transmission reach the optimal balance when the network bandwidth is unstable.

[0066] Preferably, but not limitedly, dynamically adjusting AV1 encoding parameters specifically includes:

[0067] The video encoding module further adopts an algorithm for dynamically adjusting encoding parameters based on AV1 encoding. The algorithm automatically adjusts the encoding bit rate, frame rate and resolution according to the real-time network conditions and the complexity of the video content, ensuring that the delay and packet loss rate of video transmission reach an optimal balance when the network bandwidth is unstable.

[0068] Further preferably but not limitatively, dynamically adjusting AV1 encoding parameters includes:

[0069] ① Real-time network monitoring: The current network status is obtained in real time through the network monitoring module, including bandwidth, delay, packet loss rate, etc. Preferably, but not limited to, the real-time network status is obtained through network monitoring tools such as ping and traceroute.

[0070] ② Video content analysis: The video content analysis module is used to analyze the complexity of the video content in real time, such as the degree of motion, brightness change, etc. Preferably, but not limited to, an image processing library such as OpenCV is used to perform real-time analysis of the video content.

[0071] ③Encoding parameter adjustment: Customize and dynamically adjust the encoding bit rate, frame rate, and resolution according to network conditions and video content complexity. Preferably, but not limited to, when the network bandwidth is low, reduce the bit rate and resolution; when the video content is more complex, increase the bit rate to ensure video quality.

[0072] Further preferably but not limiting, the video transmission module encrypts the video data before sending the data, using the AES-256 encryption algorithm to ensure secure data transmission and prevent the video data from being stolen and tampered with during transmission. For example but not limited to, using an encryption library such as OpenSSL to implement AES-256 encryption.

[0073] Step 3: The acquisition end sends the encoded video data to the receiving end through P2P tunnel communication. For example, but not limited to, a video transmission module is set up on the robot end, and the encoded video data is sent to the control station end through P2P tunnel communication, wherein the P2P tunnel communication is achieved by deploying a P2P tunnel in the cloud, and directly mapping the camera IP and port on the robot to the local control station through an interface call method, thereby realizing direct communication between the receiving end and the robot device, and the receiving end is, for example, but not limited to, the control station.

[0074] Specifically, the P2P tunnel is deployed in the cloud, and the camera IP and port on the robot are directly mapped to the local control station through interface calls, so as to realize direct communication between the control station and the robot device. The FEC (Forward Error Correction) mechanism is used in the transmission process to increase redundant information, reduce the number of retransmissions, and further reduce latency.

[0075] Preferably but not limiting, step 3 specifically comprises:

[0076] The P2P tunnel deployed in the cloud uses multi-path transmission. When the main path is congested or interrupted, it can automatically switch to the backup path to continue transmission, ensuring the continuity and stability of the video stream. At the same time, the intelligent path selection algorithm optimizes the path selection strategy to further improve the transmission efficiency. It is preferred, but not limited to, to use P2P communication such as WebRTC to achieve tunnel communication.

[0077] Further preferably but not limiting, the multipath transmission includes:

[0078] ① Path monitoring: The path monitoring module is used to monitor the status of each path in real time, including bandwidth, delay, packet loss rate, etc. For example, but not limited to, path monitoring tools such as Wireshark and Iperf are used to monitor the path status in real time.

[0079] ② Intelligent path selection algorithm: Select the optimal path for data transmission based on the path conditions. For example, but not limited to, when the main path is congested or interrupted, it automatically switches to the backup path. The customized intelligent path selection algorithm is used to select the optimal path for data transmission.

[0080] ③ Path switching strategy: During the path switching process, a smooth switching strategy is adopted to reduce the delay and jitter during switching.

[0081] The FEC mechanism adds redundant information during data transmission. Even if some data packets are lost in a weak signal environment, the control station can restore the video data based on the received data packets and redundant information, thereby reducing video transmission errors and retransmission times, and further reducing latency. For example, but not limited to, redundant information is added by supporting the FEC mechanism through a transmission protocol such as the QUIC protocol.

[0082] Step 4: The receiving end receives the video data sent through the P2P tunnel communication, performs data packet reorganization, and automatically reorganizes and sorts discontinuous or misordered data packets to ensure the integrity and consistency of the video data before decoding. For example, but not limited to, a video receiving module is set at the control station to receive the video data sent through the P2P tunnel communication. The receiving end has a data packet reorganization function, which automatically reorganizes and sorts discontinuous or misordered data packets to ensure the integrity and consistency of the video data before decoding and improve the smoothness of video playback.

[0083] Preferably but not limiting, step 4 specifically comprises:

[0084] Packet loss detection and recovery: Through the FEC mechanism, lost data packets are detected and recovered to ensure the integrity of video data.

[0085] Further preferably but not restrictively, an efficient feedback mechanism is established between the video transmission module and the video receiving module, and the receiving end can send quality feedback information to the acquisition end based on the decoded video quality. The sender adjusts the transmission strategy accordingly, including increasing FEC redundancy and reducing video encoding parameters, to achieve end-to-end video quality optimization.

[0086] Step 5: The receiving end uses AV1 decoding to decode the received video data and dynamically adjusts the decoding buffer size to restore the original video data. For example, but not limited to, a video decoding module is set up on the control station, and AV1 decoding is implemented using a decoding library such as FFmpeg to decode the video data to restore the original video data. At the same time, a low-latency decoding buffer algorithm is used to predict the arrival time of the video frame, and the decoding buffer size is dynamically adjusted to reduce visual delays and provide a smooth viewing experience.

[0087] Preferably but not restrictively, the video display module adopts a low-latency decoding buffer algorithm, which dynamically adjusts the decoding buffer size by predicting the arrival time of the video frame, ensuring that during the decoding and playback process, even if the network conditions change suddenly, the visual delay can be minimized to provide a smooth viewing experience.

[0088] Step 6: Display the decoded video data at the receiving end. For example, but not limited to, the video display module uses graphics libraries such as OpenGL or DirectX to achieve low-latency display and display the decoded video data. At the same time, it is combined with optimized display buffer management to reduce the delay in the display process.

[0089] Preferably but not restrictively, the video capture module on the robot side supports the collaborative work of multiple cameras, can capture video data from multiple angles at the same time, and merge multiple video streams into a single video stream for transmission through the video encoding module. The video decoding module on the control station side can accurately separate and restore multiple videos, which is suitable for application scenarios that require multi-angle observation.

[0090] Further preferably but not limiting, the multi-camera collaborative operation includes:

[0091] Multi-camera collaborative control: The video capture module on the robot supports multi-camera collaborative work, which can capture video data from multiple angles at the same time. The synchronization signal is used to ensure the consistency of video capture.

[0092] Video stream merging: Merge multiple video streams into a single video stream for transmission through the video encoding module.

[0093] Video stream separation: The video decoding module on the control station can accurately separate and restore multiple channels of video.

[0094] In summary, the present invention significantly improves the efficiency and quality of video transmission in weak signal scenarios by adopting AV1 encoding, dynamic adjustment of encoding parameter algorithm, P2P tunnel communication, forward error correction (FEC) mechanism, multi-path transmission, data packet reassembly function, low-latency decoding buffer algorithm and data encryption processing technologies, enhances the stability and security of the system, and provides strong technical support for applications such as remote control robots.

[0095] Embodiment 2 of the present invention provides a low-latency video transmission system suitable for weak signal scenarios, and runs the low-latency video transmission method suitable for weak signal scenarios as described in Embodiment 1. The acquisition end includes: a video capture module, a video encoding module, and a video transmission module; the receiving end includes: a video receiving module, a video decoding module, and a video display module.

[0096] The acquisition end, for example but not limited to, is a robot end, which is equipped with a video capture module for capturing video data in real time, wherein the video capture module mainly includes: a camera, an image sensor and an image processing unit.

[0097] Preferably but not restrictively, a high-resolution camera (1080p 60fps) on the robot side collects optical signals in real time to capture environmental video to ensure that the captured video data is of high quality; an image sensor quickly converts the optical signal into a digital signal through high-speed image capture; and an image processing unit performs preliminary processing on the captured video data, such as noise reduction, color correction, etc., to improve the video quality.

[0098] The video encoding module uses AV1 encoding and dynamically adjusts the encoding parameters based on the network conditions fed back by the real-time network monitoring module and the video complexity analyzed by the video content analysis module to ensure that the delay and packet loss rate of video transmission achieve the optimal balance when the network bandwidth is unstable.

[0099] Preferably but not restrictively, the video encoding module specifically includes: a unit for dynamically adjusting encoding parameters.

[0100] The video encoding module has a built-in algorithm for dynamically adjusting encoding parameters, including:

[0101] ① Real-time network monitoring: The network monitoring module is used to obtain the current network status in real time, including bandwidth, latency, packet loss rate, etc. For example, but not limited to, network monitoring tools (such as ping, traceroute, etc.) are used to obtain real-time network status.

[0102] ② Video content analysis: The video content analysis module is used to analyze the complexity of the video content in real time, such as the degree of motion, brightness changes, etc. For example, but not limited to, image processing libraries such as OpenCV are used to perform real-time analysis of video content.

[0103] ③Encoding parameter adjustment: Dynamically adjust the encoding bit rate, frame rate and resolution according to network conditions and video content complexity. For example, but not limited to, use a custom encoding parameter adjustment algorithm to adjust the parameters of the AV1 encoder based on network conditions and video content complexity.

[0104] For example, when the network bandwidth is low, the bit rate and resolution are reduced; when the video content is more complex, the bit rate is increased to ensure video quality.

[0105] The acquisition end also includes a data encryption module, which is used to implement AES-256 encryption algorithm through encryption libraries such as OpenSSL before sending the video data for encryption processing, to ensure the secure transmission of data and prevent the video data from being illegally intercepted or tampered with during transmission.

[0106] The video transmission module sends the encoded video data to the control station through P2P tunnel communication. The P2P tunnel is deployed in the cloud, and the camera IP and port on the robot are directly mapped to the local control station through interface calls, so as to realize direct communication between the control station and the robot device. The forward error correction (FEC) mechanism is used in the transmission process to increase redundant information, reduce the number of retransmissions, and further reduce latency. For example, but not limited to, using P2P communication such as WebRTC to realize tunnel communication.

[0107] Preferably, but not limitingly, the video transmission module uses the FEC mechanism to add redundant information during the data transmission process. Even if some data packets are lost in a weak signal environment, the control station can restore the video data based on the received data packets and redundant information, thereby reducing video transmission errors and retransmission times, and further reducing latency. For example, but not limited to, the FEC mechanism is supported by a transmission protocol such as the QUIC protocol to add redundant information.

[0108] Preferably but not limitatively, the video transmission module further includes a path optimization unit for implementing multi-path transmission, including:

[0109] ① Path monitoring: The path monitoring module uses path monitoring tools such as Wireshark and Iperf to monitor the status of each path in real time, including bandwidth, latency, packet loss rate, etc.

[0110] ② Intelligent path selection algorithm: Select the optimal path for data transmission based on the path conditions. When the main path is congested or interrupted, it automatically switches to the backup path.

[0111] ③ Path switching strategy: During the path switching process, a smooth switching strategy is adopted to reduce the delay and jitter during switching.

[0112] The video receiving module at the receiving end receives the video data sent through the P2P tunnel communication, and has the function of data packet reassembly, which can automatically reorganize and sort discontinuous or misordered data packets to ensure the integrity and consistency of the video data before decoding.

[0113] Preferably, but not restrictively, a customized data packet reassembly algorithm is used to reassemble discontinuous or misordered data packets; and a FEC mechanism is combined to detect and recover lost data packets.

[0114] The video decoding module uses FFmpeg and other decoding libraries to decode the video data AV1. It also uses a low-latency decoding buffer algorithm to predict the arrival time of video frames, dynamically adjust the decoding buffer size, reduce visual delays, and provide a smooth viewing experience.

[0115] Preferably, but not limited to, the video decoding module dynamically adjusts the decoding buffer, including: using a low-latency decoding buffer algorithm to dynamically adjust the decoding buffer size by predicting the arrival time of the video frame to ensure that when the network condition changes suddenly, the visual delay is reduced to provide a smooth viewing experience.

[0116] A video display module is set at the control station to display the decoded video data; the video display module uses a graphics library such as OpenGL or DirectX to achieve low-latency display and display the decoded video data.

[0117] Preferably, but not restrictively, the video display module uses graphics libraries such as OpenGL or DirectX to achieve low-latency display and display the decoded video data, while optimizing display buffer management to reduce delays during the display process.

[0118] Preferably but not restrictively, the video capture module on the robot side supports the collaborative work of multiple cameras, can capture video data from multiple angles at the same time, and merge multiple video streams into a single video stream for transmission through the video encoding module. The video decoding module on the control station side can accurately separate and restore multiple videos, which is suitable for application scenarios that require multi-angle observation.

[0119] Further preferably but not limiting, the multi-camera collaborative operation includes:

[0120] Multi-camera collaborative control: The video capture module on the robot supports multi-camera collaborative work, which can capture video data from multiple angles at the same time. The synchronization signal is used to ensure the consistency of video capture.

[0121] Video stream merging: Merge multiple video streams into a single video stream for transmission through the video encoding module.

[0122] Video stream separation: The video decoding module on the control station can accurately separate and restore multiple channels of video.

[0123] Embodiment 3 of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the method for low-latency video transmission in weak signal scenarios according to Embodiment 1 is implemented.

[0124] Embodiment 4 of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the low-latency video transmission method suitable for weak signal scenarios according to Embodiment 1.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A low-delay video transmission method suitable for weak signal scenarios, characterized in that: The following steps are involved: The acquisition end captures the environmental video, converts it into a digital signal, and performs preprocessing; According to the network conditions and the complexity of the video content, the pre-processed video data is encoded using AV1 encoding with dynamically adjusted encoding parameters to reduce encoding delay; The acquisition end sends the encoded video data to the receiving end through P2P tunnel communication; The receiving end receives the video data sent through the P2P tunnel communication, implements data packet reorganization, and automatically reorganizes and sorts discontinuous or misordered data packets to ensure the integrity and consistency of the video data before decoding; The receiving end decodes the received video data using AV1 decoding to restore the original video data; Display the decoded video data at the receiving end.

2. According to claim 1, a low-delay video transmission method suitable for weak signal scenarios is characterized in that: Dynamically adjust AV1 encoding parameters including: Real-time network monitoring: obtain the current network status in real time, including bandwidth, delay, and packet loss rate; Video content analysis: Real-time analysis of the complexity of video content, including: degree of motion, brightness changes; Encoding parameter adjustment: custom dynamically adjust the encoding bit rate, frame rate and resolution; Ensure that the delay and packet loss rate of video transmission reach the optimal balance when the network bandwidth is unstable.

3. According to claim 1, a low-delay video transmission method suitable for weak signal scenarios is characterized in that: The acquisition end sends the encoded video data to the receiving end through P2P tunnel communication, including: The FEC mechanism is adopted to add redundant information during the data transmission process. If some data packets are lost in a weak signal environment, the video data is restored based on the received data packets and redundant information, reducing video transmission errors and retransmission times.

4. According to claim 1, a low-delay video transmission method suitable for weak signal scenarios is characterized in that: The P2P tunnel deployed in the cloud uses multi-path transmission. If the main path is congested or interrupted, it will automatically switch to the backup path to continue transmission. Multi-path transmission includes: Path monitoring: The path monitoring module monitors the status of each path in real time, including bandwidth, delay, and packet loss rate; Intelligent path selection algorithm: select the optimal path for data transmission based on the path conditions; Path switching strategy: During the path switching process, a smooth switching strategy is adopted to reduce the delay and jitter during switching.

5. According to claim 1, a low-delay video transmission method suitable for weak signal scenarios is characterized in that: The receiving end can send quality feedback information to the acquisition end based on the decoded video quality. The sender adjusts the transmission strategy accordingly, including increasing FEC redundancy and reducing video encoding parameters, to achieve end-to-end video quality optimization.

6. According to claim 1, a low-delay video transmission method suitable for weak signal scenarios is characterized in that: The receiving end uses a low-latency decoding buffer algorithm to predict the arrival time of video frames and dynamically adjust the decoding buffer size.

7. The low-delay video transmission method suitable for weak signal scenarios according to claim 1, characterized in that: The acquisition end includes multiple cameras, which work together, including: Multi-camera collaborative control: Multiple cameras on the acquisition end work together to capture video data from multiple angles at the same time, and ensure the consistency of video capture through synchronization signals; Video stream merging: The acquisition end merges multiple video streams into a single video stream for transmission; Video stream separation: The receiving end separates and restores multiple channels of video.

8. The low-delay video transmission method suitable for weak signal scenarios according to claim 1, characterized in that: The acquisition end uses the AES-256 encryption algorithm to encrypt the video data before sending the data.

9. A low-delay video transmission system suitable for weak signal scenarios, running a low-delay video transmission method suitable for weak signal scenarios according to any one of claims 1 to 8, characterized in that: The acquisition end includes: video capture module, video encoding module, video transmission module; the receiving end includes: video receiving module, video decoding module, video display module; The video capture module is used to capture environmental video, convert it into digital signals, and perform preprocessing; The video encoding module encodes the preprocessed video data using AV1 encoding and dynamically adjusts AV1 encoding parameters; The video transmission module is used to send the encoded video data to the receiving end through P2P tunnel communication; The video receiving module is used to receive video data sent through P2P tunnel communication, implement data packet reorganization, and automatically reorganize and sort discontinuous or misordered data packets to ensure the integrity and consistency of the video data before decoding; The video decoding module is used to decode the received video data using AV1 decoding and dynamically adjust the decoding buffer size to restore the original video data; The video display module is used to use a set graphics library to achieve low-latency display and display the decoded video data.

10. A low-delay video transmission system suitable for weak signal scenarios according to claim 9, characterized in that: The video capture module includes multiple cameras working together; Multiple cameras working together capture video data from multiple angles, and the video encoding module merges multiple video streams into a single video stream for transmission. The video decoding module at the receiving end separates and restores multiple videos.

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