Method and device for optimizing Internet of Vehicles video picture transmission based on QUIC, and storage medium

Abstract

The invention relates to the technical field of Internet of Vehicles, and provides a QUIC-based optimized Internet of Vehicles video picture transmission method and device and a storage medium, and the method comprises the steps: building a QUIC connection between a vehicle end and a cloud end; the vehicle end comprises a vehicle communication module and a processing module, and the cloud end comprises a cloud end communication module and a selection module; sending a video picture request to the cloud based on the QUIC connection; determining a target video picture source through a selection module based on a preset evaluation algorithm and a matching algorithm according to a request sent by a vehicle end; according to the parameters of the target video picture source, sending parameters corresponding to the QUIC flow are determined, and a QUIC data packet corresponding to the target video picture source is sent to the vehicle side in parallel according to the sending parameters; and the processing module of the vehicle end analyzes and processes the received QUIC data packet, obtains a target video picture and displays the target video picture. According to the invention, dynamic adjustment and self-adaptive transmission of the video picture can be realized, and different scenes and requirements of the Internet of Vehicles are adapted.

Description

Technical Field

[0001] This invention relates to the field of vehicle networking technology, and in particular to a method, apparatus and storage medium for optimizing vehicle networking video and image transmission based on QUIC. Background Technology

[0002] The Internet of Vehicles (IoV) refers to a network that uses wireless communication technology to enable information exchange between vehicles, between vehicles and roads, and between vehicles and the internet. The purpose of the IoV is to improve traffic safety, efficiency, and convenience, and to achieve intelligent traffic management and services. The IoV involves various technologies, such as in-vehicle communication, sensors, positioning, cloud computing, and big data.

[0003] Currently, the video and image data volume of connected vehicles is large, requiring high-bandwidth and high-throughput transmission protocols. However, the traditional TCP protocol has drawbacks such as slow connection establishment, head-of-line congestion, and inflexible congestion control, making it unsuitable for the distribution of video and images in connected vehicles. Furthermore, the video and images in connected vehicles involve user privacy and security, requiring encrypted and authenticated transmission protocols. The traditional TCP+TLS combination increases connection establishment latency and overhead, reducing transmission efficiency.

[0004] It is known that the existing vehicle network environment is complex and variable, with problems such as high latency, packet loss, and congestion, which affect the distribution of video and images and user satisfaction. Summary of the Invention

[0005] This invention provides a method, device, and storage medium for optimizing video and image transmission in vehicle-to-everything (V2X) networks based on QUIC, in order to solve problems such as high latency, packet loss, and congestion in existing V2X networks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, embodiments of the present invention provide a method for optimizing video and image transmission in vehicle-to-everything (V2X) networks based on QUIC, applied to electronic devices, the method comprising: A QUIC connection is established between the vehicle and the cloud; wherein the vehicle includes a vehicle communication module and a processing module, and the cloud includes a cloud communication module and a selection module; The vehicle communication module sends video and image requests to the cloud based on the QUIC connection; The cloud communication module determines the target video image source based on the request sent by the vehicle terminal by selecting the module using a preset evaluation algorithm and matching algorithm. The cloud communication module determines the sending parameters of the corresponding QUIC stream based on the parameters of the target video image source, and sends the QUIC data packet corresponding to the target video image source to the vehicle terminal in parallel according to the sending parameters. The processing module parses the received QUIC data packets, obtains the target video images, and displays them.

[0007] In addition, an optional technical solution is to establish a secure session channel by using a 0-RTT or 1-RTT handshake method during the process of establishing a QUIC connection between the vehicle and the cloud. The video / image request includes parameters such as the type, size, format, resolution, importance, and deadline of the video / image.

[0008] In addition, an optional technical solution is that the evaluation algorithm includes: The availability, quality, distance, type, location, status, capacity, bandwidth, and latency information of multiple video and image sources in the candidate sources are normalized and converted into the same unit of measurement. Weights are assigned to each piece of information after normalization, with usability weighted at 0.4, quality weighted at 0.3, distance weighted at 0.2, and other information weighted at 0.1. The weighted average method was used to calculate the score for each video image source. The score = 0.4 × usability + 0.3 × quality + 0.2 × distance + 0.1 × other information. The video / image source with the highest rating is selected as the target video / image source.

[0009] In addition, an optional technical solution is that the matching algorithm includes: The video and image type, size, format, and resolution parameters in the video and image request, as well as the corresponding attributes of the video and image source, are standardized and converted into the same unit of measurement. The cosine similarity method is used to calculate the similarity between the content of the video image request and the content of the video image source. The similarity is calculated as: (the content of the video image request * the content of the video image source) / (the modulus of the video image request * the modulus of the content of the video image source). The square of the similarity is used as the matching degree for each video image source; The video / image source with the highest matching degree is selected as the target video / image source.

[0010] In addition, an optional technical solution is that the cloud communication module determines the sending parameters of the corresponding QUIC stream based on the parameters of the target video image source itself, including: The cloud communication module, based on the QUIC adaptive video and image transmission algorithm and combined with the content information, scene information, network status, and vehicle status of the video and image source, determines the sending priority, bit rate, redundancy ratio, whether to abandon near-expired non-critical fragments, and congestion control mode of each QUIC stream.

[0011] In addition, an optional technical solution is that the selection of the bit rate satisfies: b*≤T×(1-ρ)×(1-switching discount), where b* is the selected bit rate, T is the predicted throughput value, ρ is the redundancy ratio, and the switching discount is the bit rate adjustment coefficient during network switching. The redundancy ratio is determined based on the criticality of the video images and the network status; specifically, the redundancy ratio is increased in critical flows and poor network scenarios to achieve near-lossless transmission, while the redundancy ratio is reduced in non-critical flows and good network scenarios to allow for lossy transmission.

[0012] In addition, an optional technical solution is that the congestion control mode uses the BBR algorithm by default. When the network packet loss rate and RTT jitter are continuously higher than the preset threshold for several cycles, it switches to the CUBIC algorithm and switches back to the BBR algorithm after the network recovers stability. When a network switch occurs, the session is kept from being rebuilt using the QUIC connection ID, while the bitrate is reduced and the redundancy ratio is increased within a preset time period to ensure that critical flows are not interrupted.

[0013] In addition, an optional technical solution is that the vehicle-side processing module parses and processes the received QUIC data packets to obtain and display the target video images, including: The vehicle-side processing module performs sorting, reorganization, and verification operations on the metadata of the QUIC data packet. Based on the characteristics of the vehicle-side equipment, the terminal-side status, and network-side estimation, the video images are decoded, scaled, and color-adjusted to ultimately acquire and display the target video images. The processing procedure of the vehicle-side processing module includes: Reassembly and packet loss handling: Determine whether the number of received QUIC data packets is consistent with the total number, estimate the remaining data transmission time eta to the vehicle, and if eta > the deadline and the video / image is a non-critical type, then discard the fragment; Decoding priority order: Calculate the decoding priority score, sort by score from high to low, and in case of the same score, serve according to the first-come, first-served timestamp; Scaling and color adjustment: If the CPU or GPU load is less than 0.7, bicubic interpolation is used to scale the video images; otherwise, bilinear interpolation is used. The video images are converted to RGB or sRGB format using a fixed matrix, and brightness mapping is performed on the video images.

[0014] Secondly, the present invention also provides an electronic device comprising a vehicle terminal and a cloud terminal connected via QUIC, wherein the vehicle terminal includes a vehicle communication module and a processing module, and the cloud terminal includes a cloud communication module and a selection module; wherein... The vehicle communication module is used to send video and image requests to the cloud based on the QUIC connection; The cloud communication module is used to receive requests sent by the vehicle terminal, obtain the corresponding QUIC data packets, and send them back to the vehicle terminal. The selection module is used to determine the target video image source based on a preset evaluation algorithm and a matching algorithm, so as to determine the QUIC data packet according to the target video image source; The processing module is used to parse and process the received QUIC data packets, obtain the target video images, and display them.

[0015] Thirdly, the present invention also provides a computer storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the QUIC-based optimized vehicle-to-everything (V2X) video and image transmission method as described above.

[0016] By utilizing the QUIC-based optimized video and image transmission method, apparatus, and storage medium for vehicle-to-everything (V2X) networks provided by the present invention, the problems existing in the distribution of video and images in V2X networks can be solved by leveraging the characteristics of the QUIC protocol. This enables dynamic adjustment and adaptive transmission of video and images, adapting to different scenarios and needs of V2X networks. Furthermore, it protects the privacy and security of video and images, preventing eavesdropping and other forms of attacks, thus providing better services and experiences for vehicles and users.

[0017] It is understood that the electronic device described in the second aspect and the computer storage medium described in the third aspect are both used to execute the corresponding method in the first aspect provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding method provided above, and will not be repeated here. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the QUIC-based optimized vehicle-to-everything (V2X) video and image transmission method provided in this application embodiment; Figure 2 A schematic diagram of the structure of the electronic device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone.

[0021] In the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. Furthermore, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and that "first," "second," etc., do not necessarily imply differences.

[0022] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0023] The technical solution of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention can implement data transmission processing based on QUIC (Quick UDP Internet Connections). QUIC is a transport layer protocol based on the User Datagram Protocol (UDP), designed to improve the speed and reliability of network connections, replacing the Transmission Control Protocol (TCP) widely used in the current Internet infrastructure.

[0025] This invention provides a method for optimizing video and image transmission in vehicle-to-everything (V2X) networks based on QUIC. (Refer to...) Figure 1 The diagram shown is a flowchart illustrating the QUIC-based optimized video and image transmission method for vehicle-to-everything (V2X) networks provided in Embodiment 1 of the present invention. This method can be executed by an electronic device, which can be implemented in software and / or hardware.

[0026] In this embodiment, the QUIC-based optimized vehicle-to-everything (V2X) video and image transmission method includes: S100: Establish a QUIC connection between the vehicle and the cloud; wherein the vehicle includes a vehicle communication module and a processing module, and the cloud includes a cloud communication module and a selection module.

[0027] In the process of establishing a QUIC connection between the vehicle and the cloud, a zero- or one-round-trip (0-RTT or 1-RTT) handshake can be used to quickly establish the connection, thereby reducing the latency during connection establishment and establishing a secure session channel between the two ends through the 0-RTT or 1-RTT handshake method.

[0028] S200: The vehicle communication module sends video and image requests to the cloud based on the QUIC connection.

[0029] The video and image request must include at least the following parameters: type, size, format, resolution, importance, and deadline.

[0030] S300: The cloud communication module determines the target video image source based on the request sent by the vehicle terminal by selecting the module based on a preset evaluation algorithm and matching algorithm.

[0031] Specifically, the evaluation algorithm may further include: S311: Normalize the availability, quality, distance, type, location, status, capacity, bandwidth, and latency information of multiple video and image sources in the candidate sources and convert them into the same unit of measurement; S312: Assign weights to each piece of information after normalization, where usability is weighted at 0.4, quality at 0.3, distance at 0.2, and other information at 0.1; S313: Calculate the score for each video / image source using a weighted average method. Score = 0.4 × Usability + 0.3 × Quality + 0.2 × Distance + 0.1 × Other Information; S314: Select the video / image source with the highest rating as the target video / image source.

[0032] Furthermore, the matching algorithm may further include: S21: Standardize the video / image type, size, format, and resolution parameters in the video / image request, as well as the corresponding attributes of the video / image source, and convert them into the same unit of measurement; S322: The cosine similarity method is used to calculate the similarity between the content of the video image request and the content of the video image source. The similarity is calculated as (the content of the video image request * the content of the video image source) / (the modulus of the video image request * the modulus of the content of the video image source). S323: Use the square of the similarity as the matching degree for each video image source; S324: Select the video image source with the highest matching degree as the target video image source.

[0033] S400: The cloud communication module determines the sending parameters of the corresponding QUIC stream based on the parameters of the target video image source, and sends the QUIC data packet corresponding to the target video image source to the vehicle terminal in parallel according to the sending parameters.

[0034] Specifically, the cloud communication module determines the sending parameters of the corresponding QUIC stream based on the parameters of the target video image source, including: The cloud communication module, based on the QUIC adaptive video and image transmission algorithm and combined with the content information, scene information, network status, and vehicle status of the video and image source, determines the sending priority, bit rate, redundancy ratio, whether to abandon near-expired non-critical fragments, and congestion control mode of each QUIC stream.

[0035] The QUIC-based adaptive video and image transmission algorithm includes the following steps: based on the content information of the video and image, such as type, criticality, deadline, and size; scene information, such as normal, nighttime, rain / fog, tunnel, and driving altitude; network status, such as RTT jitter, packet loss rate, handover risk, and throughput prediction; and vehicle-side status, such as playback buffer size, CPU or GPU load, and temperature, the algorithm outputs the sending priority, bitrate, redundancy ratio, whether to abandon near-expiring non-critical fragments, and congestion control mode for each QUIC stream.

[0036] The bitrate selection satisfies: b*≤T×(1-ρ)×(1-switching discount), where b* is the selected bitrate, T is the predicted throughput, ρ is the redundancy ratio, and the switching discount is the bitrate adjustment coefficient during network switching. The redundancy ratio is determined based on the criticality of the video images and the network status. Specifically, the redundancy ratio is increased in critical flows and poor network scenarios to achieve near-lossless transmission, while the redundancy ratio is reduced in non-critical flows and good network scenarios to allow for lossy transmission.

[0037] As a specific example, the congestion control mode uses the BBR algorithm by default. When the network packet loss rate and RTT jitter are continuously higher than the preset threshold for several cycles, it switches to the CUBIC algorithm and switches back to the BBR algorithm after the network recovers and stabilizes. When a network switch occurs, the session is kept from being rebuilt by using the QUIC connection ID, and the bit rate is reduced and the redundancy ratio is increased within a preset time period to ensure that critical flows are not interrupted.

[0038] S500: The processing module on the vehicle side parses and processes the received QUIC data packets, obtains the target video image, and displays it.

[0039] Specifically, this step may further include: S510: The vehicle-side processing module performs sorting, reorganization, and verification operations on the metadata of the QUIC data packet; S520: Based on the characteristics of the vehicle-side equipment, the terminal-side status, and network-side estimation, it decodes, scales, and adjusts the colors of video images to ultimately acquire and display the target video images.

[0040] Specifically, the processing procedure of the aforementioned vehicle-side processing module mainly includes: 1. Reassembly and packet loss handling: Determine whether the number of received QUIC data packets is consistent with the total number, estimate the remaining data transmission time eta to the vehicle end, and if eta > the deadline and the video / image is a non-critical type, then discard the fragment; 2. Decoding priority sorting: Calculate the decoding priority score, sort by score from high to low, and in case of the same score, serve according to the first-come, first-served timestamp; 3. Scaling and Color Adjustment: If the CPU or GPU load is less than 0.7, bicubic interpolation is used to scale the video images; otherwise, bilinear interpolation is used. 4. Convert the video images to RGB or sRGB format using a fixed matrix, and perform brightness mapping on the video images.

[0041] Specifically, the QUIC-based optimized vehicle-to-everything (V2X) video and image transmission method of this invention mainly includes the following: Step 1: Establishing a QUIC connection: The vehicle communication module on the vehicle side acts as a QUIC protocol client, and the cloud communication module on the cloud side acts as a QUIC protocol server. The two establish a QUIC connection through a UDP port. During the connection establishment process, a 0-RTT or 1-RTT handshake method is used to significantly reduce the connection establishment latency and lay the foundation for subsequent data transmission.

[0042] Step 2: Video / Image Source Selection: The vehicle sends video / image requests to the cloud via the established QUIC connection. The request includes parameters such as the type of video / image (e.g., navigation, monitoring, entertainment, education), size, format, resolution, criticality (critical / non-critical), and deadline. Upon receiving the request, the cloud's selection module executes an evaluation and matching algorithm for the video / image sources, ultimately selecting the optimal source from multiple sources as the target video / image source.

[0043] Step 3: Cloud Data Transmission: The cloud communication module, based on the QUIC adaptive video and image transmission algorithm, combines video and image content information (type, criticality, deadline, size), scene information (normal / night / rain / fog / tunnel / high-speed driving), network status (throughput prediction T, RTT jitter, packet loss rate, handover risk), and vehicle-side status (playback buffer size B, CPU / GPU load, temperature) to determine the transmission priority, bitrate, redundancy ratio, whether to abandon near-expiring non-critical fragments, and congestion control mode for each QUIC stream. Then, according to the determined parameters, multiple video and image data streams are sent in parallel to the vehicle via the QUIC connection. The multiplexing feature of the QUIC protocol avoids head-of-line congestion and improves transmission efficiency.

[0044] The bitrate selection satisfies b*≤T×(1-ρ)×(1-switching discount), where b* represents the final selected transmission bitrate (bits / s), i.e., the actual transmission bitrate selected by the cloud for the current video / image stream; T represents the predicted available bandwidth estimated in real time by QUIC's BBR / CUBIC statistics module; and ρ (rho) represents the redundancy ratio, used to mitigate common issues in vehicle-to-everything (V2X) networks such as packet loss and jitter. For example, ρ=0.1 indicates that 10% bandwidth needs to be reserved for redundancy, FEC, and retransmission. This ensures the bitrate matches the network bandwidth. The redundancy ratio is dynamically adjusted based on the criticality of the video / image and network status. In critical flows and poor network conditions, the redundancy ratio is increased to approach lossless transmission; in non-critical flows and good network conditions, the redundancy ratio is decreased to allow for lossy transmission. The congestion control mode defaults to the BBR algorithm. When the network packet loss rate and RTT jitter continuously exceed a preset threshold, it switches to the CUBIC algorithm, and reverts to the BBR algorithm after the network stabilizes. During network switching, the session is kept from being rebuilt using the QUIC connection ID, while the bitrate and redundancy ratio are briefly adjusted to ensure that critical flows are not interrupted.

[0045] Step 4: Vehicle-side Data Processing and Display: After receiving the QUIC data packets sent from the cloud, the vehicle-side communication module transmits them to the processing module. The processing module first performs sorting, reassembly, and verification operations based on the data packet's metadata, such as stream_id, seq, total, size, and type. Then, based on the vehicle-side device characteristics, such as resolution, brightness, color, and end-side status (e.g., CPU and GPU load) and network-side estimation (e.g., available throughput T), it performs the following processing: (1) Reassembly and packet loss handling: Determine whether the number of received data packets is consistent with the total number, estimate the remaining data transmission time eta, and if eta > the deadline and the video or image is a non-critical type, then discard the segment to free up resources for critical content.

[0046] (2) Decoding sorting: Calculate the decoding priority score score = (Is it critical content? 1:0.3) / video / image size × (1 / max(ε, deadline - current time)). Sort by score from high to low. In case of the same score, serve by stream_id first to ensure that critical, small-sized, and near-deadline video / images are decoded first.

[0047] When decoding received video and image segments, the system prioritizes them based on three factors: 1. Content criticality: Critical content (e.g., navigation screens, monitoring screens) is decoded first; non-critical content (e.g., entertainment, educational screens) follows. 2. Segment size: Smaller segments decode faster and are therefore processed first; larger segments take longer and are processed later. 3. Deadline urgency: Segments closer to the display deadline have higher priority; less urgent segments can be processed later. The system assigns a "priority level" to each segment based on these three factors and decodes them from highest to lowest priority. If multiple segments are at the same priority level, they are processed according to their stream_id or arrival order, ensuring that critical, small, and soon-to-expire content is displayed first.

[0048] (3) Scaling and color adjustment: If the CPU / GPU load is <0.7, bicubic interpolation is used for scaling; otherwise, bilinear interpolation is used. The format of the video image is converted to RGB or sRGB format through a fixed matrix, and the brightness is mapped using the formula L_out=L / (1+L) to adapt the video image to the vehicle display device and finally complete the display. Here, L represents the original brightness (the larger the value, the brighter the pixel, and the smaller the value, the darker the pixel), L_out is the brightness output after compression, and "1" is used to control the intensity of brightness compression (by adding "1" to the denominator, the growth rate of the overly bright area can be slowed down to prevent overexposure). The purpose is to prevent overexposure and enhance the display effect.

[0049] Corresponding to the above-mentioned QUIC-based optimized vehicle network video and image transmission method, the present invention also provides an electronic device.

[0050] like Figure 2As shown, the electronic device of this embodiment includes a vehicle terminal and a cloud terminal connected by QUIC. The vehicle terminal includes a vehicle communication module and a processing module, and the cloud terminal includes a cloud communication module and a selection module. The vehicle communication module is used to send video / image requests to the cloud terminal based on the QUIC connection. The cloud communication module is used to receive requests sent by the vehicle terminal, obtain corresponding QUIC data packets, and feed them back to the vehicle terminal. The selection module is used to determine the target video / image source based on a preset evaluation algorithm and a matching algorithm, and to determine the QUIC data packet based on the target video / image source. The processing module is used to parse and process the received QUIC data packets, obtain the target video / image, and display it.

[0051] Specifically, the vehicle-side equipment includes a communication module and a processing module; The vehicle-side communication module is used to establish a QUIC connection with the communication module of the cloud device through a UDP port as a QUIC protocol client. It adopts a 0-RTT or 1-RTT handshake method to reduce the connection establishment latency, sends video and image requests and receives QUIC data packets sent by the cloud device. At the same time, it utilizes the multiplexing, reliability, security and connection migration characteristics of the QUIC protocol to ensure data transmission efficiency, integrity, security and connection stability during network switching. The processing module is used to receive QUIC data packets transmitted by the communication module, sort, reassemble, and verify the data packet metadata, decode the video images using the corresponding decoder based on the characteristics of the vehicle terminal device, the terminal status, and the network side estimation, and perform scaling and color adjustment through an adaptive algorithm to complete the display of the video images. The cloud device includes a cloud communication module and a selection module; The selection module is used to select the optimal video image source from multiple video image sources based on the video image source evaluation algorithm and matching algorithm, combined with the vehicle-side request parameters. The cloud communication module is used to establish a QUIC connection with the vehicle-side device's communication module via a UDP port, acting as a QUIC protocol server. It employs a 0-RTT or 1-RTT handshake to reduce connection establishment latency, receives video and image requests from the vehicle-side device, determines QUIC stream parameters based on the QUIC adaptive video and image transmission algorithm, and sends multiple video and image data streams to the vehicle-side device in parallel through the QUIC connection. Simultaneously, it utilizes the multiplexing, reliability, security, and connection migration characteristics of the QUIC protocol to ensure data transmission efficiency, integrity, security, and connection stability during network switching.

[0052] As specific examples, the present invention will be described in detail below through two embodiments.

[0053] Example 1: Video transmission in a high-speed driving network switching scenario First, the scenario parameters are set as follows: the vehicle is traveling at high speed, and the network switches from Wi-Fi to 4G; the vehicle simultaneously requests navigation video (critical type, 200KB, deadline 0.15s) and entertainment video (non-critical type, 300KB, deadline 2s); the network status is a throughput prediction T=6Mbps, with some RTT jitter and switching risk; the vehicle's CPU / GPU load is 0.5, and the playback buffer size is sufficient.

[0054] During cloud processing, the selection module uses video and image source evaluation and matching algorithms to determine that the navigation video source is locally stored (highest score) and the entertainment video source is the internet (highest matching degree). The cloud communication module executes the QUIC adaptive video and image transmission algorithm, calculating that the priority score of the navigation video stream is higher than that of the entertainment video stream. The bitrate of the navigation video stream is determined to be 4Mbps, with a redundancy ratio of approximately 10%, and BBR congestion control mode is adopted; the bitrate of the entertainment video stream is 1-2Mbps, with a redundancy ratio of approximately 5%, and the same BBR congestion control mode is adopted. During network switching, the bitrate of the entertainment video stream is briefly reduced to 1Mbps, and the redundancy ratio is increased to 8%, while the parameters of the navigation video stream remain unchanged.

[0055] During vehicle-side processing, the vehicle-side communication module receives two video data streams via a QUIC connection. It utilizes multiplexing to process them in parallel, and maintains a stable connection during network switching thanks to connection migration features, ensuring no data interruption. The processing module calculates the navigation video stream's e^ta as eta = now + 200KB / 6Mbps ≈ 0.27s, which is less than the deadline of 0.15s (this is just an example calculation; actual calculations need to be adjusted based on real-time network conditions), so it does not discard the data. It calculates the decoding priority score as score ≈ (1 / 200KB) × (1 / 0.15), which is higher than the entertainment video stream's score ≈ (0.3 / 300KB) × (1 / 2), therefore, the navigation video is decoded first. Since the CPU / GPU load is 0.5 < 0.7, both video streams are scaled using bicubic interpolation. Color format conversion and brightness mapping are performed using a fixed matrix, resulting in a clear and smooth navigation video display. While the entertainment video has a lower bitrate, it exhibits no noticeable stuttering. After network switching, the entertainment video bitrate recovers to 2Mbps.

[0056] Example 2: Image transmission in low-bandwidth scenarios during rainy and foggy weather First, the scenario parameters are set as follows: the vehicle is driving in rainy or foggy weather, the network bandwidth is low, the predicted throughput T=2Mbps, and the packet loss rate is high; the vehicle requests monitoring images (critical type, 100KB, deadline 0.5s) and educational images (non-critical type, 150KB, deadline 3s); the vehicle's CPU / GPU load is 0.85, and the display device resolution is low.

[0057] During cloud processing, the selection module determines that the source of monitoring images is roadside base station storage (close distance, low latency), and the source of educational images is the cloud server (highest matching degree).

[0058] The cloud communication module executes the QUIC adaptive video and image transmission algorithm. The monitoring image stream has a high priority score, a fixed bitrate of 1.5Mbps, and a redundancy ratio of approximately 15%. Due to the high packet loss rate, the congestion control mode is switched to CUBIC. The education image stream has a fixed bitrate of 0.5Mbps and a redundancy ratio of approximately 3%, also using the CUBIC congestion control mode. The estimated latency of the education image stream is eta = now + 150KB / 2Mbps ≈ 0.6s, which is less than the 3-second deadline, so it is not discarded.

[0059] During vehicle-side processing, the vehicle-side communication module receives two channels of image data and utilizes the reliability characteristics of the QUIC protocol to recover lost packet data, ensuring the integrity of the monitoring image data.

[0060] The processing module prioritizes decoding surveillance images. Due to the CPU / GPU load ratio of 0.85 > 0.7, bilinear interpolation is used for scaling to adapt to low-resolution display devices. Educational images are decoded later, also using bilinear interpolation for scaling. The final surveillance images are clearly displayed, meeting monitoring requirements. Although the educational images have lower resolution, they are still viewable without data loss.

[0061] It should be noted that the descriptions in the above-described methods and apparatus embodiments can be used as a reference for each other, and will not be repeated here.

[0062] As can be seen from the above embodiments, the method and apparatus of the present invention can effectively adapt to different scenarios of vehicle networking, solve the defects of traditional TCP protocol, and realize efficient, reliable, and secure transmission and display of video and images, and have the following beneficial effects: 1. Improve transmission efficiency and quality: By leveraging the low latency and multiplexing characteristics of the QUIC protocol, the problems of slow connection establishment and head-of-line blocking in the traditional TCP protocol are solved, significantly improving the transmission efficiency of video and images in the vehicle network; by dynamically adjusting the bit rate, redundancy ratio and congestion control mode, it adapts to the complex and ever-changing network environment of the vehicle network and reduces the impact of packet loss and congestion on transmission quality.

[0063] 2. Achieve adaptive transmission: Based on the content information, scene information, network status, and vehicle status of video and images, the transmission parameters are dynamically adjusted to achieve lossy and lossless transmission of video and images, balancing the integrity and real-time performance of video and images, and meeting the needs of different vehicle networking scenarios (navigation, monitoring, entertainment, education).

[0064] 3. Ensure data security and privacy: Leveraging the end-to-end encryption and authentication features of the QUIC protocol based on TLS, the video and image data of the vehicle network are encrypted and protected throughout the entire process to prevent data from being eavesdropped on, tampered with, or attacked, thus ensuring user privacy and data security.

[0065] 4. Optimize display effects and user experience: The vehicle-side processing module dynamically adjusts the scaling and color of video images based on device characteristics to ensure clear and adaptable display on different display devices; by prioritizing the processing of critical content and discarding non-critical near-term segments, the continuity and stability of core services are guaranteed, significantly improving the user experience.

[0066] Figure 3 Only electronic devices with components are shown, including a QUIC-optimized vehicle-to-everything (V2X) video and image transmission program. These electronic devices can be installed within electronic devices, as will be understood by those skilled in the art. Figure 3 The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0067] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor via a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be elaborated further here.

[0068] Furthermore, the electronic device may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device and other electronic devices.

[0069] Optionally, the electronic device may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0070] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0071] Furthermore, if the modules / units of the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0073] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional modules 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 in the form of hardware plus software functional modules.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A QUIC-based optimized video and image transmission method for vehicle-to-everything (V2X) networks, applied to electronic devices, characterized in that... The method includes: A QUIC connection is established between the vehicle and the cloud; wherein the vehicle includes a vehicle communication module and a processing module, and the cloud includes a cloud communication module and a selection module; The vehicle communication module sends video and image requests to the cloud based on the QUIC connection; The cloud communication module determines the target video image source based on the request sent by the vehicle terminal and the selection module using a preset evaluation algorithm and matching algorithm. The cloud communication module determines the sending parameters of the corresponding QUIC stream based on the parameters of the target video image source, and sends the QUIC data packet corresponding to the target video image source to the vehicle terminal in parallel according to the sending parameters. The vehicle-side processing module parses and processes the received QUIC data packets to obtain and display the target video images.

2. The method for optimizing vehicle-to-everything (V2X) video and image transmission based on QUIC as described in claim 1, characterized in that, During the process of establishing a QUIC connection between the vehicle and the cloud, a secure session channel is established using a 0-RTT or 1-RTT handshake method. The video / image request includes parameters such as the type, size, format, resolution, importance, and deadline of the video / image.

3. The method for optimizing vehicle-to-everything (V2X) video and image transmission based on QUIC as described in claim 1, characterized in that, The evaluation algorithm includes: The availability, quality, distance, type, location, status, capacity, bandwidth, and latency information of multiple video and image sources in the candidate sources are normalized and converted into the same unit of measurement. Weights are assigned to each piece of information after normalization, with usability weighted at 0.4, quality weighted at 0.3, distance weighted at 0.2, and other information weighted at 0.

1. The weighted average method was used to calculate the score for each video image source. The score = 0.4 × usability + 0.3 × quality + 0.2 × distance + 0.1 × other information. The video / image source with the highest rating is selected as the target video / image source.

4. The method for optimizing vehicle-to-everything (V2X) video and image transmission based on QUIC as described in claim 3, characterized in that, The matching algorithm includes: The video and image type, size, format, and resolution parameters in the video and image request, as well as the corresponding attributes of the video and image source, are standardized and converted into the same unit of measurement. The cosine similarity method is used to calculate the similarity between the content of the video image request and the content of the video image source. The similarity is calculated as: (the content of the video image request * the content of the video image source) / (the modulus of the video image request * the modulus of the content of the video image source). The square of the similarity is used as the matching degree for each video image source; The video / image source with the highest matching degree is selected as the target video / image source.

5. The method for optimizing vehicle-to-everything (V2X) video and image transmission based on QUIC as described in claim 1, characterized in that, The cloud communication module determines the sending parameters of the corresponding QUIC stream based on the parameters of the target video image source, including: The cloud communication module, based on the QUIC adaptive video and image transmission algorithm and combined with the content information, scene information, network status, and vehicle status of the video and image source, determines the sending priority, bit rate, redundancy ratio, whether to abandon near-expired non-critical fragments, and congestion control mode of each QUIC stream.

6. The QUIC-based optimized vehicle-to-everything (V2X) video and image transmission method as described in claim 5, characterized in that, The selection of the bit rate satisfies: b*≤T×(1-ρ)×(1-switching discount), where b* is the selected bit rate, T is the predicted throughput value, ρ is the redundancy ratio, and the switching discount is the bit rate adjustment coefficient during network switching. The redundancy ratio is determined based on the criticality of the video images and the network status; specifically, the redundancy ratio is increased in critical flows and poor network scenarios to achieve near-lossless transmission, while the redundancy ratio is reduced in non-critical flows and good network scenarios to allow for lossy transmission.

7. The QUIC-based optimized vehicle-to-everything (V2X) video and image transmission method as described in claim 5, characterized in that, The congestion control mode defaults to the BBR algorithm. When the network packet loss rate and RTT jitter are continuously higher than the preset threshold for several cycles, it switches to the CUBIC algorithm and switches back to the BBR algorithm after the network recovers stability. When a network switch occurs, the session is kept from being rebuilt using the QUIC connection ID, while the bitrate is reduced and the redundancy ratio is increased within a preset time period to ensure that critical flows are not interrupted.

8. The method for optimizing vehicle-to-everything (V2X) video and image transmission based on QUIC as described in claim 5, characterized in that, The vehicle-side processing module parses and processes the received QUIC data packets, obtains the target video images, and displays them, including: The vehicle-side processing module performs sorting, reorganization, and verification operations on the metadata of the QUIC data packet. Based on the characteristics of the vehicle-side equipment, the terminal-side status, and network-side estimation, the video images are decoded, scaled, and color-adjusted to ultimately acquire and display the target video images. The processing procedure of the vehicle-side processing module includes: Reassembly and packet loss handling: Determine whether the number of received QUIC data packets is consistent with the total number, estimate the remaining data transmission time eta to the vehicle, and if eta > the deadline and the video / image is a non-critical type, then discard the fragment; Decoding priority order: Calculate the decoding priority score, sort by score from high to low, and in case of the same score, serve according to the first-come, first-served timestamp; Scaling and color adjustment: If the CPU or GPU load is less than 0.7, bicubic interpolation is used to scale the video images; otherwise, bilinear interpolation is used. The video images are converted to RGB or sRGB format using a fixed matrix, and brightness mapping is performed on the video images.

9. An electronic device, characterized in that, This includes a QUIC-connected vehicle terminal and a cloud terminal. The vehicle terminal includes a vehicle communication module and a processing module, and the cloud terminal includes a cloud communication module and a selection module. The vehicle communication module is used to send video and image requests to the cloud based on the QUIC connection; The cloud communication module is used to receive requests sent by the vehicle terminal, obtain the corresponding QUIC data packets, and send them back to the vehicle terminal. The selection module is used to determine the target video image source based on a preset evaluation algorithm and a matching algorithm, so as to determine the QUIC data packet according to the target video image source; The processing module is used to parse and process the received QUIC data packets, obtain the target video images, and display them.

10. A computer storage medium, characterized in that, The computer-readable storage medium stores instructions, characterized in that, when the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the QUIC-based optimized vehicle network video and image transmission method as described in any one of claims 1-8.

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