Video aabr method, device and system based on 5g mobile information opening

Abstract

The application provides a video ABR method, device and system based on 5G mobile information opening, the method comprising: receiving a current ABR decision generated by a client of a mobile 5G network service for requesting to reduce the code rate of a next video segment based on MEC, and querying the current mobile network switching information and the instant video buffer length of the client; judging whether the current ABR decision is a misjudgment decision suitable for video playback quality optimization based on the mobile network switching information and the instant video buffer length, if yes, rejecting to execute the misjudgment decision and transmitting the next video segment to the client at the current video segment code rate corresponding to the client. The application can realize the optimization of video playback quality based on 5G mobile switching perception, effectively improve the video playback code rate of the user, and reduce the video picture code rate jitter rate and picture freezing rate, thereby effectively improving the quality of experience QoE of the user playing the video in a mobile scenario.

Description

Technical Field

[0001] This application relates to the field of video stream quality control technology, and in particular to video ABR methods, devices and systems based on 5G mobile information openness. Background Technology

[0002] With the rapid development of 5G, video providers are seeking key technologies to improve streaming quality in order to gain greater commercial profits. It is well known that 5G provides users with ample physical layer bandwidth resources, theoretically reaching 1Gbps, which can meet the needs of all current video applications, including virtual reality (VR) video. Academic research in static scenarios has also verified the reliability of 5G's high bandwidth. Unfortunately, for cellular networks (3G / 4G / 5G), mobile and static scenarios differ significantly. In the 3G / 4G era, research showed that mobile scenarios have a significant negative impact on cellular network bandwidth and latency, even causing serious problems for video applications such as abnormal replays, prolonged video stuttering, and connection interruptions. Although 5G is believed to support ultra-high mobile speeds of 500km / h, its gains for video applications in mobile scenarios are quite limited, making new breakthroughs in video applications urgently needed.

[0003] Currently, in the video application field, most application service providers have adopted the Dynamic Adaptive HTTP Streaming (DASH) method to improve user service quality (QoE). Unfortunately, the video industry currently lacks effective optimization solutions for video playback quality under mobile 5G networks, which is the crux of the challenge in improving video user QoE. Summary of the Invention

[0004] In view of this, embodiments of this application provide a video ABR method, apparatus and system based on 5G mobile information openness to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of this application provides a video ABR method based on 5G mobile information openness, including:

[0006] The client, which receives 5G network services via MEC, generates the current ABR decision for requesting a reduction in the bitrate of the next video segment, and queries the client's current mobile network handover information and real-time video buffer length.

[0007] Based on the mobile network handover information and the real-time video buffer length, determine whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization. If so, refuse to execute the misjudgment decision and transmit the next video segment to the client at the current video segment bitrate corresponding to the client.

[0008] In some embodiments of this application, the current ABR decision generated by the client receiving mobile 5G network services based on MEC for requesting a reduction in the bitrate of the next video segment, and querying the client's current mobile network handover information and real-time video buffer length, includes:

[0009] The resource request message sent by the client receiving the mobile 5G network service based on MEC contains the current ABR decision and the instantaneous video buffer length generated by the client for requesting a reduction in the bitrate of the next video segment.

[0010] In addition, the system collects mobile network switching information from clients of the 5G mobile network service in real time, wherein the mobile network switching information includes: switching timestamp and switching type.

[0011] In some embodiments of this application, the step of determining whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization based on the mobile network handover information and the real-time video buffer length, and if so, rejecting the misjudgment decision and transmitting the next video segment to the client at the current video segment bitrate corresponding to the client, includes:

[0012] Based on the switching timestamp of the current mobile network switching information, determine whether a mobile network switching event has occurred after the client generates the previous ABR decision. If so, obtain the preset detection result of the impact of mobile switching on video playback quality.

[0013] Based on the detection results of the impact of mobile handover on video playback quality, determine whether the handover type of the current mobile network handover information belongs to the network capability optimization type.

[0014] If the switching type belongs to the network capability optimization type, then determine whether the length of the real-time video buffer is equal to or greater than the duration threshold.

[0015] If the length of the instantaneous video buffer is equal to or greater than the duration threshold, the current ABR decision is determined to be a misjudgment decision applicable to video playback quality optimization, and the misjudgment decision is rejected and the next video segment is transmitted to the client at the current video segment bitrate corresponding to the client.

[0016] In some embodiments of this application, it also includes:

[0017] The experimental bed device receives playback requests for various video segments based on ABR decision sent through a mobile 5G network, wherein each video segment playback request corresponds to a different resolution and bitrate;

[0018] Data is sent to the experimental bed device according to the playback requests of each video segment, so that the experimental bed device records different types of video application metrics and collects the interaction signaling between itself and the 5G base station in real time when it switches, so that the experimental bed device can perform time alignment and qualitative analysis processing based on the interaction signaling and video stream events, and generate corresponding detection results of the impact of mobile switching on video playback quality.

[0019] Receive and store the detection results of the impact of the mobile switching on video playback quality sent by the experimental bed equipment.

[0020] In some embodiments of this application, the experimental bed device performs time alignment and qualitative analysis processing based on the interactive signaling and video stream events to generate corresponding detection results of the impact of motion switching on video playback quality, including:

[0021] Based on the interactive signaling and video stream events, the experimental bed device locally constructs a timing diagram of ABR bitrate adjustment decision and real-time QoE index change.

[0022] Based on the ABR bitrate adjustment decision and the time series diagram of real-time QoE index changes, the bitrate adjustment decision is divided into correct decisions and incorrect decisions, so as to generate the corresponding mobile handover impact detection results on video playback quality.

[0023] In some embodiments of this application, the detection results of the impact of the mobile handover on video playback quality include:

[0024] For the ABR decision used to request a reduction in the bitrate of the next video segment, if the handover type corresponding to the mobile network handover event belongs to the network capability optimization type and the instantaneous video buffer length is equal to or greater than the duration threshold, then the ABR decision is determined to be a misjudgment decision applicable to video playback quality optimization.

[0025] For the ABR decision used to request a reduction in the bitrate of the next video segment, if the handover type corresponding to the mobile network handover event does not belong to the network capability optimization type or the instantaneous video buffer length is less than the duration threshold, then the ABR decision is determined to be a correct decision that is not applicable to video playback quality optimization.

[0026] The network capability optimization types include: 5G to 5G, 4G to 5G, and 4G to 4G.

[0027] Another aspect of this application provides a video ABR device based on 5G mobile information openness, comprising:

[0028] The query module is used to generate the current ABR decision for requesting a reduction in the bitrate of the next video segment based on the client receiving mobile 5G network services via MEC, and to query the client's current mobile network handover information and real-time video buffer length.

[0029] The optimization module is used to determine whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization based on the mobile network handover information and the real-time video buffer length. If so, the module refuses to execute the misjudgment decision and transmits the next video segment to the client at the current video segment bitrate corresponding to the client.

[0030] Another aspect of this application provides a video playback quality measurement and optimization system under a mobile 5G network, including:

[0031] The server is used to execute the video playback quality measurement and optimization method under the mobile 5G network.

[0032] The experimental bed device is mounted on a mobile carrier and is connected to the server via a mobile 5G network. The experimental bed device is used to record different types of video application metrics and collect the interaction signaling between itself and the 5G base station in real time when it switches. Based on the interaction signaling and video stream events, time alignment and qualitative analysis are performed to generate corresponding mobile switching impact detection results on video playback quality, and the mobile switching impact detection results are sent to the server.

[0033] Another aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned video ABR method based on 5G mobile information openness.

[0034] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the aforementioned video ABR method based on 5G mobile information openness.

[0035] The video ABR method based on 5G mobile information openness provided in this application generates a current ABR decision for requesting a reduction in the bitrate of the next video segment from a client receiving 5G network services via MEC, and queries the client's current mobile network handover information and real-time video buffer length. Based on the mobile network handover information and real-time video buffer length, it determines whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization. If so, it rejects the misjudgment decision and transmits the next video segment to the client at the current video segment bitrate corresponding to the client. It can optimize video playback quality based on 5G mobile handover awareness, effectively improve the user's video playback bitrate, and reduce video bitrate jitter and stuttering rate, thereby effectively improving the user's quality of experience (QoE) for video playback in mobile or non-mobile scenarios. Furthermore, based on 5G network openness functions, it can supplement the DASH protocol ABR algorithm with a handover-aware bitrate adjustment strategy on the server side in a backend server-driven manner, achieving a stable improvement in QoE for mobile video users in a user-unnoticed, low-overhead, and easy-to-deploy manner.

[0036] Additional advantages, objectives, and features of this application will be set forth in part in the description which follows, and will in part become apparent to those skilled in the art upon review of the following description, or may be learned by practice of the application. The objectives and other advantages of this application can be realized and obtained by means of the structures specifically pointed out in the specification and drawings.

[0037] Those skilled in the art will understand that the purposes and advantages that can be achieved with this application are not limited to those specifically described above, and that the above and other purposes that this application can achieve will be more clearly understood from the following detailed description. Attached Figure Description

[0038] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, do not constitute a limitation thereof. The components in the drawings are not drawn to scale but are merely for illustrating the principles of this application. For ease of illustration and description of certain parts of this application, corresponding portions in the drawings may be enlarged, i.e., may appear larger relative to other components in an exemplary device actually manufactured according to this application. In the drawings:

[0039] Figure 1 This is a schematic diagram of the overall process of a video ABR method based on 5G mobile information access in one embodiment of this application.

[0040] Figure 2 This is a schematic diagram of a specific process of a video ABR method based on 5G mobile information access in one embodiment of this application.

[0041] Figure 3This is a schematic diagram illustrating another specific process of the video ABR method based on 5G mobile information access in one embodiment of this application.

[0042] Figure 4 This is a schematic diagram of the structure of a video ABR device based on 5G mobile information access in another embodiment of this application.

[0043] Figure 5 This is a schematic diagram illustrating an example of a 5G network end-to-end DASH video application testbed provided in the application examples of this application.

[0044] Figure 6 This is a schematic diagram illustrating the timing of ABR rate adjustment decisions and real-time QoE metric changes provided in the application examples of this application.

[0045] Figure 7 This is a schematic diagram illustrating the optimization of the ABR algorithm based on handover awareness, provided in an application example of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and their descriptions are used to explain this application, but are not intended to limit it.

[0047] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the structures and / or processing steps closely related to the solution according to this application are shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0048] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0049] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0050] In the following description, embodiments of the present application will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0051] Over the past decade, video streaming applications have experienced tremendous growth. It is estimated that by 2022, short videos, multi-bitrate video-on-demand, and live video will account for more than 80% of internet traffic, and this figure is expected to continue to grow. Meanwhile, since 2019, 5G has developed rapidly. As of November 2021, more than 1.2 million 5G base stations have been deployed globally, with my country accounting for more than half, making it a significant representative of global 5G network deployment and research.

[0052] Existing Dynamic Adaptive HTTP Streaming (DASH) uses the Adaptive Bitrate (ABR) algorithm as the core of its video segment playback decisions. Generally, current ABR algorithms can be divided into four categories:

[0053] (i) Based on throughput, it predicts available network bandwidth and selects a maximum bitrate not exceeding the throughput estimate;

[0054] (ii) Based on the length of the buffered video area, it takes into account the current buffer filling situation and tries to maintain a stable buffer, thereby reducing the risk of stuttering;

[0055] (iii) Dynamic hybrid strategy, which advocates combining the use of buffer length and throughput estimation to make full use of bandwidth and improve QoE;

[0056] (iiii) Machine learning / deep learning algorithms use complex modeling methods to learn and train algorithm models in the hope of making better decisions in the face of fluctuating network changes. However, considering the limited computing resources of users on the end, they are rarely used in the current DASH streaming media service.

[0057] Overall, in the feedback signals from the user application layer player, the accuracy of throughput estimation and the buffer size are crucial for subsequent ABR decisions. However, in mobile scenarios, user movement causes frequent 5G cell handovers by the user equipment (UE), resulting in incorrect throughput estimations or abrupt changes in buffer length at the application layer. Existing ABR algorithms for DASH video applications struggle to respond correctly in this fluctuating network environment.

[0058] Although current ABR algorithms are becoming increasingly advanced, such as the widely used Bola and Dynamic algorithms, they are still more suitable for static / low-speed mobile scenarios (where network fluctuations are relatively stable) and cannot adapt to the frequent 5G handovers caused by rapid movement. The main reason is:

[0059] (i) The network throughput change caused by switching during movement is short-term but drastic. However, the ABR algorithm is unaware of the underlying network switching and can easily mistakenly assume that the network condition will deteriorate, thereby reducing the playback bitrate to reduce stuttering.

[0060] (ii) To ensure a relatively stable video playback bitrate, the video bitrate often takes a long time to recover after being reduced by the ABR algorithm, resulting in consistently low user QoE. In summary, the existing ABR algorithm does not distinguish between non-congested network transitions (cell handover) in the last hop of the underlying network (from the base station to the UE), thus underestimating network bandwidth and generating long-term low QoE, failing to utilize the abundant network resources of 5G.

[0061] The ABR algorithm for DASH video streams is typically a set of strategies, including throughput estimation, stable bitrate fluctuations, and stuttering prevention. A strategy for real-time QoE decision correction specifically for mobile 5G network scenarios is urgently needed. Meanwhile, although the development of relevant standards and protocols in video application fields is relatively slow in some regions, there is enormous potential. Therefore, it is necessary to seize the key opportunities presented by the development of 5G and video applications, develop video transmission optimization algorithms, and strive to establish a dominant position in related fields.

[0062] In one or more embodiments of this application, the 5G mobile information openness refers to exposing the handover information of the mobile UE to the video server through devices such as MEC. This is also referred to in this application as "the current ABR decision generated by the client receiving mobile 5G network services via MEC for requesting a reduction in the bitrate of the next video segment".

[0063] In one or more embodiments of this application, ABR (Adaptive Bitrate) refers to an adaptive bitrate algorithm, and the video ABR method refers to a video bitrate adaptive method, which adaptively requests videos with different bitrates from the server for playback based on network conditions.

[0064] The following examples will provide a detailed description.

[0065] Based on this, in order to improve the video playback bitrate and enhance the user's video playback quality (QoE) in both mobile and non-mobile scenarios, this application provides a server-executed video ABR method based on 5G mobile information openness. See [link to relevant documentation]. Figure 1 The video ABR method based on 5G mobile information specifically includes the following:

[0066] Step 100: The client receiving the mobile 5G network service based on MEC generates the current ABR decision for requesting a reduction in the bitrate of the next video segment, and queries the client's current mobile network handover information and real-time video buffer length.

[0067] In step 100, the server can specifically use the current ABR decision generated by the client receiving the mobile 5G network service based on the MEC to request a reduction in the bitrate of the next video segment, that is, expose the handover information of the mobile UE to the server through the MEC.

[0068] It is understandable that the embodiments of this application design a handover-aware ABR optimization scheme for DASH video streaming applications. The successful implementation of this scheme relies on the increasingly sophisticated 5G service framework. It is important to clarify that 5G is not merely an enhancement of network bandwidth; its more significant meaning lies in its RAN management and core network layered design, which are distinct from 3G / 4G. Regarding RAN management, the 5G framework uses Multi-Access Edge Computing (MEC, whose industry standard is jointly developed by 3GPP and the European organization ETSI) as a crucial module to achieve ultra-low latency and Network Open Functions (NEF), which exposes underlying UE information accessing the 5G network, such as cell identifier, handover events, signal quality, and bandwidth, to application provider servers. In terms of the core network, 5G decentralizes some core network functions to the RAN, further enhancing RAN capabilities. These prerequisites provide favorable conditions for the design optimization of this application.

[0069] Step 200: Based on the mobile network handover information and the real-time video buffer length, determine whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization. If so, refuse to execute the misjudgment decision and transmit the next video segment to the client at the current video segment bitrate corresponding to the client.

[0070] It is understood that in this application embodiment, the bitrate adjustment decision of the ABR algorithm is actively intervened by the backend server. Although the ABR algorithm is executed and decided on the UE side, it ultimately requests the download of the video segment with the specified bitrate from the server based on the decision result. This application can monitor these decisions on the backend and actively correct the decision when it detects that the ABR has made an incorrect decision due to mobile handover. This backend-driven optimization method can avoid modifications to the UE client application and is easy to deploy on a large scale.

[0071] In other words, the embodiments of this application are based on 5G Network Exposure Function (NEF) and Multi-access Edge Computing (MEC). Without making any modifications to the local UE, the DASH protocol ABR algorithm is supplemented with a handover-aware rate adjustment strategy on the server side in a backend server-driven manner. This helps the mobile UE correct erroneous ABR behavior caused by user handover, and has the significant advantages of being imperceptible to the user and having low overhead.

[0072] As described above, the video ABR method based on 5G mobile information openness provided in this application can optimize video playback quality based on 5G mobile handover awareness, effectively improve the user's video playback bitrate, and reduce video bitrate jitter and stuttering rate, thereby effectively improving the user's video playback quality of experience (QoE) in mobile or non-mobile scenarios. Furthermore, based on the open functions of 5G network, and driven by a backend server, it can supplement the DASH protocol ABR algorithm with a handover awareness-based bitrate adjustment strategy on the server side, achieving a stable improvement in QoE for mobile video users in a user-unnoticed, low-overhead, and easy-to-deploy manner.

[0073] To further improve the accuracy and reliability of determining whether video playback quality can be optimized, a video ABR method based on 5G mobile information openness is provided in this application embodiment, see [link to relevant documentation]. Figure 2 Step 100 of the video ABR method based on 5G mobile information openness also specifically includes the following:

[0074] Step 110: Receive a resource request message sent by the client of the mobile 5G network service based on MEC. The resource request message contains the current ABR decision and the instantaneous video buffer length generated by the client for requesting a reduction in the bitrate of the next video segment.

[0075] Understandably, each time the UE client's ABR algorithm requests a reduction in the bitrate of the next video segment, the server-side DASH-H intervenes in the decision-making process. It queries the time of the last mobile handover event and the instantaneous video buffer length in the DASH client resource request message.

[0076] Step 120: Collect mobile network switching information of the client of the mobile 5G network service in real time, wherein the mobile network switching information includes: switching timestamp and switching type.

[0077] Understandably, based on the open RAN network capabilities provided by MEC, the DASH video application server can obtain the mobile handover information of the UE it serves in real time, including the handover timestamp and handover type. The existing 5G is a non-standalone network, and its handover types include 5G→5G, 5G→4G, 4G→5G, and 4G→4G.

[0078] To further improve the accuracy and reliability of determining whether video playback quality can be optimized, a video ABR method based on 5G mobile information openness is provided in this application embodiment, see [link to relevant documentation]. Figure 2 Step 200 of the video ABR method based on 5G mobile information openness also specifically includes the following:

[0079] Step 210: Determine whether a mobile network handover event has occurred after the client generated the previous ABR decision based on the handover timestamp of the current mobile network handover information. If so, proceed to step 220: Obtain the preset detection results of the impact of mobile handover on video playback quality.

[0080] Step 230: Based on the detection results of the impact of mobile handover on video playback quality, determine whether the handover type of the current mobile network handover information belongs to the network capability optimization type;

[0081] If the switching type belongs to the network capability optimization type, then step 240 is executed: determine whether the length of the real-time video buffer is equal to or greater than the duration threshold;

[0082] If the length of the instantaneous video buffer is equal to or greater than the duration threshold, then step 250 is executed: determine that the current ABR decision is a misjudgment decision applicable to video playback quality optimization, and refuse to execute the misjudgment decision and transmit the next video segment to the client at the current video segment bitrate corresponding to the client.

[0083] Specifically, the type of handover can be used to determine whether network capabilities will continue to decline. Of the four handover types mentioned above, only 5G→4G indicates a failed 5G handover, with the UE accessing 4G and resulting in a decrease in network capabilities. The other three handover types only cause a data interruption of approximately 90 milliseconds at the physical layer and will not cause a long-term deterioration in network performance. Therefore, the network has sufficient capacity to guarantee the quality of application layer data transmission.

[0084] For example, if a motion switching event has occurred since the last ABR decision was made, and the video buffer is large enough (≥5 seconds), DASH-H will prevent the ABR from reducing the bit rate and will transmit a video segment to the client at the original bit rate.

[0085] Since ABR algorithm decisions are generally relatively conservative, although increasing the video bitrate will improve the user's QoE, insufficient network bandwidth may lead to video stuttering. This requires this application to have a sufficient understanding of the mobile 5G network status. Therefore, in order to further improve the reliability of video playback quality optimization applications, synchronous quantitative measurements and qualitative analyses were performed at the physical layer, transport layer, and application layer of the 5G network. In the video ABR method based on 5G mobile information openness provided in the embodiments of this application, a bottom-down system measurement methodology for video stream QoE correlation metrics is also provided. See [link to relevant documentation]. Figure 3 The video ABR method based on 5G mobile information openness, prior to step 100, specifically includes the following:

[0086] Step 010: Receive playback requests for various video segments based on ABR decision sent by the experimental bed device through the mobile 5G network, wherein each video segment playback request has a different resolution and bitrate.

[0087] Step 020: Data is sent to the experimental bed device according to the playback requests of each video segment, so that the experimental bed device records different types of video application metrics and collects the interaction signaling between itself and the 5G base station in real time when it switches, so that the experimental bed device can perform time alignment and qualitative analysis processing based on the interaction signaling and video stream events to generate corresponding detection results of the impact of mobile handover on video playback quality.

[0088] Step 030: Receive and store the detection results of the impact of the motion switching on video playback quality sent by the experimental bed device.

[0089] Specifically, to conduct quantitative and qualitative analysis of the QoE of DASH video streams under mobile 5G (NSA 3a architecture, non-standalone) networks, this application builds a comprehensive DASH video end-to-end measurement platform based on the widely used dash.js framework. In driving / high-speed rail scenarios, the client accesses the DASH streaming media server via the 5G network, requesting and playing video segments with different resolution:bitrate values ​​(including 360P: 1.2Mbps, 720P: 3.6Mbps, 1080P: 7.2Mbps, 2K: 12.0Mbps, 4K: 26.8Mbps; higher resolutions have higher bitrates and require higher network bandwidth) to match instantaneous network bandwidth fluctuations. During the actual measurement process, this application records two types of video application metrics, such as:

[0090] (i) ABR decision information of the DASH protocol, including video bitrate adjustment and the effect of ABR policy family, such as "prevent bitrate fluctuation" and "abandon current download";

[0091] (ii) Video stream QoE metrics, including bitrate, video segment throughput, stuttering rate, resolution, and buffer length. This application has developed a large amount of trace recording code within the dash.js framework to facilitate the collection, offline processing, and quantitative calculation of the above metrics at fine-grained, long-term scales.

[0092] In addition, this application uses the professional software PCNET to collect the interaction signaling between the user and the base station in real time when the UE is switching in a mobile scenario, and performs time alignment and qualitative analysis with the upper-layer DASH video stream events in offline analysis.

[0093] To further improve the reliability of video playback quality optimization applications, in a video ABR method based on 5G mobile information openness provided in this application embodiment, the experimental bed device in step 020 of the video ABR method based on 5G mobile information openness performs time alignment and qualitative analysis processing based on the interactive signaling and video stream events to generate corresponding detection results of the impact of mobile handover on video playback quality, specifically including the following:

[0094] Based on the interactive signaling and video stream events, the experimental bed device locally constructs a timing diagram of ABR bitrate adjustment decision and real-time QoE index change.

[0095] Based on the ABR bitrate adjustment decision and the time series diagram of real-time QoE index changes, the bitrate adjustment decision is divided into correct decisions and incorrect decisions, so as to generate the corresponding mobile handover impact detection results on video playback quality.

[0096] Specifically, this application, through extensive measurement experiments, found that the average bandwidth of 5G networks in mobile scenarios is significantly lower than in static scenarios. Taking the high-speed rail scenario as an example, in static outdoor scenarios, the 5G network bandwidth is close to 1Gbps, while on high-speed trains (250-350km / h), its bandwidth drops to only about 100Mbps. Despite this, theoretically, it still has sufficient capacity to support smooth high-bitrate (resolution) video playback. However, measurement results show that during the experiment, only 37.4% of the time was spent playing the highest bitrate (4K, 26.8Mbps) video segments, and even for 2.3% of the time, the video was choppy. More surprisingly, this application found that over 64% of the video segments actually had a download throughput exceeding 26.8Mbps, meaning the client downloaded a large number of low-bitrate video segments at high throughput, indicating a severe mismatch between application-layer video QoE and the underlying 5G network capabilities. This application, through synchronous comparative analysis of mobile handover events and ABR decisions, discovered the root cause of this problem.

[0097] To further improve the reliability of video playback quality optimization applications, in a video ABR method based on 5G mobile information openness provided in this application embodiment, the detection result of the impact of mobile handover on video playback quality in the video ABR method based on 5G mobile information openness specifically includes the following:

[0098] For the ABR decision used to request a reduction in the bitrate of the next video segment, if the handover type corresponding to the mobile network handover event belongs to the network capability optimization type and the instantaneous video buffer length is equal to or greater than the duration threshold, then the ABR decision is determined to be a misjudgment decision applicable to video playback quality optimization.

[0099] For the ABR decision used to request a reduction in the bitrate of the next video segment, if the handover type corresponding to the mobile network handover event does not belong to the network capability optimization type or the instantaneous video buffer length is less than the duration threshold, then the ABR decision is determined to be a correct decision that is not applicable to video playback quality optimization.

[0100] The network capability optimization types include: 5G to 5G, 4G to 5G, and 4G to 4G.

[0101] In summary, mobile handover from the underlying 5G network causes a short-term but significant drop in network performance at the upper layers, severely misleading the ABR decision-making algorithm of upper-layer applications and reducing the QoE of DASH applications.

[0102] From a software perspective, in order to improve the video playback bitrate and enhance the user's video playback quality (QoE) in both mobile and non-mobile scenarios, this application also provides a 5G mobile information access-based video ABR apparatus for executing all or part of the 5G mobile information access-based video ABR method. See [link to relevant documentation]. Figure 4 The video ABR device based on 5G mobile information specifically includes the following components:

[0103] The query module 10 is used to generate the current ABR decision for requesting a reduction in the bitrate of the next video segment based on the client receiving mobile 5G network services via MEC, and to query the client's current mobile network handover information and real-time video buffer length.

[0104] The optimization module 20 is used to determine whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization based on the mobile network handover information and the real-time video buffer length. If so, the misjudgment decision is rejected and the next video segment is transmitted to the client at the current video segment bitrate corresponding to the client.

[0105] The embodiments of the video ABR device based on 5G mobile information openness provided in this application can be used to execute the processing flow of the embodiment of the video ABR method based on 5G mobile information openness in the above embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above embodiment of the video ABR method based on 5G mobile information openness.

[0106] The 5G mobile information access-based video ABR device can perform the 5G mobile information access-based video ABR portion on a server, or in another practical application scenario, all operations can be completed on the client device. The choice depends on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed on the client device, the client device may further include a processor for the specific processing of the 5G mobile information access-based video ABR.

[0107] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0108] The server and the client device can communicate using any suitable network protocol, including those not yet developed as of the date of this application. Such network protocols may include, for example, TCP / IP, UDP / IP, HTTP, HTTPS, etc. Furthermore, such network protocols may also include RPC (Remote Procedure Call Protocol) and REST (Representational State Transfer Protocol) protocols used on top of the aforementioned protocols.

[0109] As can be seen from the above description, the video ABR device based on 5G mobile information openness provided in this application embodiment can optimize video playback quality based on 5G mobile handover awareness, effectively improve the user's video playback bitrate, and reduce video bitrate jitter and stuttering rate, thereby effectively improving the user's video playback quality of experience (QoE) in mobile or non-mobile scenarios; and based on the 5G network openness function, it can supplement the DASH protocol ABR algorithm with a handover awareness-based bitrate adjustment strategy on the server side in a backend server-driven manner, so as to achieve a stable improvement in the QoE of mobile video users in a user-unnoticed, low-overhead, and easy-to-deploy manner.

[0110] Based on the aforementioned embodiments of the video ABR method or device based on 5G mobile information openness, this application also provides a video playback quality measurement and optimization system under a mobile 5G network. The video playback quality measurement and optimization system under a mobile 5G network specifically includes the following:

[0111] (1) A server, used to execute the video playback quality measurement and optimization method under the mobile 5G network;

[0112] (2) Experimental bed device, which is set on a mobile carrier and is connected to the server via a mobile 5G network. The experimental bed device is used to record different types of video application metrics and collect the interactive signaling between itself and the 5G base station in real time when it switches. Based on the interactive signaling and video stream events, time alignment and qualitative analysis are performed to generate the corresponding mobile switching impact on video playback quality detection results, and the mobile switching impact on video playback quality detection results are sent to the server.

[0113] To further illustrate this solution, this application also provides a specific application example of a video ABR method based on 5G mobile information openness. Specifically, this application application example designs a DASH supplementary protocol for ABR decision intervention based on handover awareness, called DASH-H.

[0114] The technical problems addressed by the application examples in this application are mainly in two aspects: analysis of the capabilities of existing 5G networks and supplementation to the DASH protocol. Specifically: (i) ABR algorithm decisions are generally relatively conservative. Although increasing the video bitrate will improve the user's QoE, insufficient network bandwidth may lead to video stuttering. This requires this application to have a sufficient understanding of the mobile 5G network status. (ii) The DASH protocol generally places the ABR algorithm decision-making and execution on the client side. If this application directly modifies the ABR algorithm, it will require large-scale patching or upgrading of the DASH application.

[0115] Therefore, to address the above issues, this application uses typical mobile scenarios, namely driving and high-speed rail, as experimental scenarios:

[0116] (i) A thorough measurement and analysis of the 5G networks on highways and high-speed railways was conducted;

[0117] (ii) A supplementary ABR strategy was designed for the DASH protocol that is applicable to mobile scenarios and compatible with non-mobile scenarios.

[0118] The core improvements in the application examples of this application are as follows:

[0119] (1) A bottom-up systematic measurement methodology for video stream QoE correlation is proposed. This application uses an application example to build an end-to-end measurement platform and use dedicated cellular signaling monitoring software to perform synchronous quantitative measurements and qualitative analyses at the physical layer, transport layer, and application layer of 5G networks, providing a comprehensive system-level understanding of the handover characteristics of mobile 5G networks, cross-layer network performance differences, and DASH video ABR decision failures.

[0120] (2) Based on the 5G Network Exposure Function (NEF) and Multi-access Edge Computing (MEC), without any modifications to the local UE, the DASH protocol ABR algorithm is supplemented with a handover-aware rate adjustment strategy on the server side in a back-end server-driven manner, which helps the mobile UE correct the erroneous ABR behavior caused by user handover. It has the significant advantages of being imperceptible to the user and having low overhead.

[0121] The technical solutions used in the application examples of this application include the following:

[0122] (I) Experimental Bed Construction and Measurement Methods for End-to-End DASH Video Application in 5G Networks

[0123] To conduct quantitative and qualitative analysis of the QoE of DASH video streams under mobile 5G (NSA 3a architecture, non-standalone) networks, this application example builds a complete DASH video end-to-end measurement platform based on the widely used dash.js framework. In driving / high-speed rail scenarios, the client accesses the DASH streaming media server via the 5G network, requesting and playing video segments with different resolution:bitrate (including 360P: 1.2Mbps, 720P: 3.6Mbps, 1080P: 7.2Mbps, 2K: 12.0Mbps, 4K: 26.8Mbps; higher resolution results in higher bitrate and greater network bandwidth requirements) to match instantaneous network bandwidth fluctuations. In the actual measurement process, this application example recorded two types of video application metrics, such as: (i) ABR decision information of the DASH protocol, including the video bitrate adjustment and the effect of ABR policy families, such as "preventing bitrate fluctuations" and "abandoning the current download"; (ii) video stream QoE metrics, including bitrate, video segment throughput, stuttering rate, resolution, and buffer length. This application example further developed a large amount of trace recording code within the dash.js framework to facilitate the collection, offline processing, and quantitative calculation of the above metrics at fine-grained, long-term scales.

[0124] In addition, this application example uses the professional software PCNET to collect real-time signaling interactions between the user and the base station during UE handover in mobile scenarios. In offline analysis, this signaling is time-aligned and qualitatively analyzed with upper-layer DASH video stream events. For the overall end-to-end testbed, see [link to testbed]. Figure 5 .

[0125] (II) Detailed Analysis of the Impact of Mobile Handover on the DASH ABR Algorithm

[0126] Extensive measurement experiments conducted in this application example revealed a significant decrease in the average bandwidth of 5G networks in mobile scenarios compared to static scenarios. Taking high-speed rail as an example, in static outdoor scenarios, the 5G network bandwidth approaches 1Gbps, while on high-speed trains (250-350km / h), it drops to only about 100Mbps. Despite this, theoretically, it still has sufficient capacity to support smooth high-bitrate (resolution) video playback. However, measurement results show that only 37.4% of the time during the experiment was spent playing the highest bitrate (4K, 26.8Mbps) video segments, and even during 2.3% of the time, the video was choppy. More surprisingly, this application found that over 64% of the video segments actually had a download throughput exceeding 26.8Mbps, meaning the client downloaded a large number of low-bitrate video segments at high throughput, indicating a severe mismatch between application-layer video QoE and the underlying 5G network capabilities. Through synchronous comparative analysis of mobile handover events and ABR decisions, this application example identified the root cause of this problem.

[0127] See Figure 6 Using real-world cases recorded in high-speed rail scenarios, this study explains how mobile handover affects the ABR (Automatic Back-End) decisions of DASH applications.

[0128] Figure 6 The upper half records the bitrate adjustments made by ABR decisions (A1-A11), and the lower half shows the segment throughput (Seg.Thro.) and buffer level. This application reveals 11 ABR decisions that reduce the video bitrate, marked with down arrows. Up arrows represent ABR decisions that increase the bitrate. HO indicates a motion handover occurred at the current moment, and Buffer indicates a buffer.

[0129] This application categorizes the aforementioned bitrate adjustment behavior into two types:

[0130] (i){A1, A2, A3, A5, A11}, before the ABR decision on these 5 actions, the video segment throughput is low or the buffer length drops significantly due to the influence of mobile handover. In the case of insufficient buffer, the ABR algorithm naturally lowers its subsequent bitrate selection in order to avoid stuttering events. This application defines it as the correct ABR decision.

[0131] (ii) {A4, A6, A7, A8, A9, A10} Although the mobile handover caused several short-term throughput or buffer length reductions, the buffer was still sufficient (greater than 5 seconds) when these 6 actions were executed. This application can consider that the ABR's down-rate behavior is a misjudgment caused by an incorrect estimate of network capacity. Therefore, this application actively prevented the ABR's bitrate down-rate decision in real experiments. This adjustment did not cause video stuttering and significantly increased the playback bitrate, thereby improving the user's QoE.

[0132] In summary, mobile handover from the underlying 5G network causes a short-term but significant drop in network performance at the upper layers, severely misleading the ABR decision-making algorithm of upper-layer applications and reducing the QoE of DASH applications.

[0133] (III) Optimization of DASH ABR Algorithm Based on Handover Awareness

[0134] Inspired by the measurement and analysis above, this application example designs a handover-aware ABR optimization scheme for DASH video streaming applications. The successful implementation of this scheme relies on the increasingly sophisticated 5G service framework. It is important to clarify that 5G is not merely an enhancement of network bandwidth; its more significant meaning lies in its RAN management and core network layered design, which are significantly different from 3G / 4G. Regarding RAN management, the 5G framework uses Multi-Access Edge Computing (MEC, whose industry standard is jointly developed by 3GPP and the European organization ETSI) as a crucial module to achieve ultra-low latency and Network Open Functions (NEF), which exposes underlying UE information accessing the 5G network, such as cell identifier, handover events, signal quality, and bandwidth, to application provider servers. In terms of the core network, 5G decentralizes some core network functions to the RAN, further enhancing RAN capabilities. These prerequisites provide favorable conditions for the design optimization of this application example.

[0135] In general, the overall optimization approach for the application examples in this application is as follows:

[0136] (1) Based on the open RAN network capabilities provided by MEC, the DASH video application server can obtain the mobile handover information of the UE it serves in real time, including the handover timestamp and handover type. The existing 5G is a non-standalone network, and its handover types are 5G→5G, 5G→4G, 4G→5G, and 4G→4G.

[0137] (2) The type of handover can be used to determine whether the network capability will continue to decline. Of the four handover types mentioned above, except for 5G→4G which indicates that the 5G handover has failed and the UE accesses 4G, resulting in a decline in network capability, the other three handover types will only cause a data interruption of about 90 milliseconds at the physical layer and will not cause a long-term decline in network performance. Therefore, the network has sufficient capability to ensure the quality of application layer data transmission.

[0138] (3) Active intervention in the bitrate adjustment decision of the ABR algorithm by the backend server. Although the ABR algorithm is executed and makes decisions at the UE end, it ultimately requests the download of the video segment with the specified bitrate from the server based on the decision result. This application can monitor these decisions in the backend and actively correct the decision when it finds that the ABR has made an incorrect decision due to mobile handover. This backend-driven optimization method can avoid modifications to the UE client application and is easy to deploy on a large scale.

[0139] See Figure 7 The application example uses the following optimization strategy for the DASH protocol ABR algorithm in the backend driver: Figure 7 The lower horizontal axis represents the video segments downloaded in sequence (Seg1-7), the upper horizontal axis marks motion switching events, and the vertical axis represents the resolution of the video segment (also representing the bitrate). Figure 7 Each column of squares represents a video block with the same sequence number but at different resolutions (bitrates). From video segment Seg1 to video segment Seg7, the resolutions of the video blocks downloaded by the UE are 2K→2K→2K→360P→4K→4K→720P, respectively.

[0140] The optimization strategy DASH-H in this application is as follows: Each time the UE client's ABR algorithm requests a reduction in the bitrate of the next video segment, the server-side DASH-H intervenes in the decision-making process. It queries the time of the last mobile handover event and the instantaneous video buffer length in the DASH client resource request message, and selects one of the following decisions based on this:

[0141] Type A: If a move switching event has occurred since the last ABR decision (marked with the symbol "●") and the video buffer is large enough (≥5 seconds), DASH-H will prevent the ABR from reducing the bitrate (marked with "×") and will transmit a video segment to the client at the original bitrate.

[0142] Type B: If the buffer is insufficient after a mobile switch (e.g., due to 5G→4G, the buffer is only 1 second), DASH-H will allow this decision to reduce the bitrate to avoid video stuttering (marked as...). ).

[0143] If the HO event does not affect the bit rate determination of the ABR algorithm (Type C), or if there is no recent HO event, meaning the bit rate drop is due to normal network bandwidth fluctuations (Type D), DASH-H will approve the decision. In short, DASH-H shields against inaccurate ABR decisions caused by HO events.

[0144] Based on the above optimizations, the application examples in this application will significantly improve the service QoE of DASH video applications in mobile scenarios.

[0145] In summary, this application proposes a bottom-up systematic measurement methodology for video stream QoE-related metrics. This application performs synchronous quantitative measurement and qualitative analysis at the physical, transport, and application layers of the 5G network, comprehensively and systematically exploring the cross-layer correlation between handover characteristics of mobile 5G networks and DASH video performance. Based on the open functions of the 5G network, this application uses a backend server-driven approach to supplement the DASH protocol's ABR algorithm with a handover-aware rate adjustment strategy on the server side, achieving a stable improvement in QoE for mobile video users in a user-unobtrusive, low-overhead, and easily deployable manner.

[0146] The DASH video QoE optimization based on 5G mobile handover awareness proposed in this application example is an important verification of the openness of 5G network capabilities and has pioneering guiding significance. This application example has realistically verified the performance improvement of this design for DASH applications in 5G live networks in driving, high-speed rail, and walking scenarios. Taking the high-speed rail scenario as an example, compared with the corresponding baseline method, this application example increased the user's video playback bitrate by approximately 2 times and reduced the video bitrate jitter and stuttering rate by 70.2% and 49.6%, respectively. For the three scenarios, the percentage of highest bitrate (4K) video playback increased by 37.4%→65.3%, 51.4%→85.7%, and 73.5%→95.4%, respectively. This demonstrates that the solution proposed in this application example can bring significant improvements to DASH video applications in mobile scenarios, and the success of this design strategy also has reference value for the optimization of other future applications.

[0147] This application also provides an electronic device, which may include a processor, a memory, a receiver, and a transmitter. The processor is used to execute the video ABR method based on 5G mobile information openness mentioned in the above embodiments. The processor and memory can be connected via a bus or other means, taking a bus connection as an example. The receiver can be connected to the processor and memory via wired or wireless means. The electronic device can receive real-time motion data from sensors in the wireless multimedia sensor network and receive raw video sequences from the video acquisition device.

[0148] The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0149] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the video ABR method based on 5G mobile information access in the embodiments of this application. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the video ABR method based on 5G mobile information access in the above method embodiments.

[0150] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] The one or more modules are stored in the memory, and when executed by the processor, the video ABR method based on 5G mobile information openness in the implementation embodiment is executed.

[0152] In some embodiments of this application, the user equipment may include a processor, a memory, and a transceiver unit. The transceiver unit may include a receiver and a transmitter. The processor, memory, receiver, and transmitter may be connected via a bus system. The memory is used to store computer instructions, and the processor is used to execute the computer instructions stored in the memory to control the transceiver unit to send and receive signals.

[0153] As one implementation method, the functions of the receiver and transmitter in this application can be implemented by transceiver circuits or dedicated transceiver chips, and the processor can be implemented by dedicated processing chips, processing circuits or general-purpose chips.

[0154] As another implementation approach, the server provided in this application embodiment can be implemented using a general-purpose computer. That is, the program code implementing the processor, receiver, and transmitter functions is stored in memory, and the general-purpose processor implements the processor, receiver, and transmitter functions by executing the code in memory.

[0155] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned video ABR method based on 5G mobile information openness. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, floppy disk, hard disk, removable storage disk, CD-ROM, or any other form of storage medium known in the art.

[0156] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave.

[0157] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0158] In this application, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0159] The above description is merely a preferred embodiment of this application and is not intended to limit this application. For those skilled in the art, various modifications and variations can be made to the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A video ABR method based on 5G mobile information openness, characterized in that, include: The client, which receives 5G network services via MEC, generates the current ABR decision for requesting a reduction in the bitrate of the next video segment, and queries the client's current mobile network handover information and real-time video buffer length. Based on the mobile network handover information and the real-time video buffer length, determine whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization. If so, refuse to execute the misjudgment decision and transmit the next video segment to the client at the current video segment bitrate corresponding to the client. The client, which receives 5G network services via MEC, generates a current ABR decision to request a reduction in the bitrate of the next video segment, and queries the client's current mobile network handover information and real-time video buffer length, including: The resource request message sent by the client receiving the mobile 5G network service based on MEC contains the current ABR decision and the instantaneous video buffer length generated by the client for requesting a reduction in the bitrate of the next video segment. In addition, the mobile network switching information of the client of the mobile 5G network service is collected in real time, wherein the mobile network switching information includes: switching timestamp and switching type; The step of determining whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization based on the mobile network handover information and the real-time video buffer length, and if so, rejecting the misjudgment decision and transmitting the next video segment to the client at the current video segment bitrate corresponding to the client, includes: Based on the switching timestamp of the current mobile network switching information, determine whether a mobile network switching event occurred after the client generated the previous ABR decision. If so, obtain the preset detection result of the impact of mobile switching on video playback quality. Based on the detection results of the impact of mobile handover on video playback quality, determine whether the handover type of the current mobile network handover information belongs to the network capability optimization type. If the switching type belongs to the network capability optimization type, then determine whether the length of the real-time video buffer is equal to or greater than the duration threshold. If the length of the instant video buffer is equal to or greater than the duration threshold, the current ABR decision is determined to be a misjudgment decision applicable to video playback quality optimization, and the misjudgment decision is rejected and the next video segment is transmitted to the client at the current video segment bitrate corresponding to the client. The network capability optimization types include: 5G to 5G, 4G to 5G, and 4G to 4G.

2. The video ABR method based on 5G mobile information openness according to claim 1, characterized in that, Also includes: The experimental bed device receives playback requests for various video segments based on ABR decision sent through a mobile 5G network, wherein each video segment playback request corresponds to a different resolution and bitrate; Data is sent to the experimental bed device according to the playback requests of each video segment, so that the experimental bed device records different types of video application metrics and collects the interaction signaling between itself and the 5G base station in real time when it switches, so that the experimental bed device can perform time alignment and qualitative analysis processing based on the interaction signaling and video stream events, and generate corresponding detection results of the impact of mobile switching on video playback quality. Receive and store the detection results of the impact of the mobile switching on video playback quality sent by the experimental bed equipment.

3. The video ABR method based on 5G mobile information openness according to claim 2, characterized in that, The experimental bed device performs time alignment and qualitative analysis based on the interactive signaling and video stream events to generate corresponding detection results of the impact of motion switching on video playback quality, including: Based on the interactive signaling and video stream events, the experimental bed device locally constructs a timing diagram of ABR bitrate adjustment decision and real-time QoE index change. Based on the ABR bitrate adjustment decision and the time series diagram of real-time QoE index changes, the bitrate adjustment decision is divided into correct decisions and incorrect decisions, so as to generate the corresponding mobile handover impact detection results on video playback quality.

4. The video ABR method based on 5G mobile information openness according to any one of claims 1 to 3, characterized in that, The detection results of the impact of mobile switching on video playback quality include: For the ABR decision used to request a reduction in the bitrate of the next video segment, if the handover type corresponding to the mobile network handover event belongs to the network capability optimization type and the instantaneous video buffer length is equal to or greater than the duration threshold, then the ABR decision is determined to be a misjudgment decision applicable to video playback quality optimization. For the ABR decision used to request a reduction in the bitrate of the next video segment, if the handover type corresponding to the mobile network handover event does not belong to the network capability optimization type or the instantaneous video buffer length is less than the duration threshold, then the ABR decision is determined to be a correct decision that is not applicable to video playback quality optimization.

5. A video ABR device based on 5G mobile information openness, characterized in that, include: The query module is used to generate the current ABR decision for requesting a reduction in the bitrate of the next video segment based on the client receiving mobile 5G network services via MEC, and to query the client's current mobile network handover information and real-time video buffer length. The optimization module is used to determine whether the current ABR decision is a misjudgment decision suitable for video playback quality optimization based on the mobile network handover information and the real-time video buffer length. If so, the misjudgment decision is rejected and the next video segment is transmitted to the client at the current video segment bitrate corresponding to the client. The client, which receives 5G network services via MEC, generates a current ABR decision to request a reduction in the bitrate of the next video segment, and queries the client's current mobile network handover information and real-time video buffer length, including: The resource request message sent by the client receiving the mobile 5G network service based on MEC contains the current ABR decision and the instantaneous video buffer length generated by the client for requesting a reduction in the bitrate of the next video segment. In addition, the mobile network switching information of the client of the mobile 5G network service is collected in real time, wherein the mobile network switching information includes: switching timestamp and switching type; The step of determining whether the current ABR decision is a misjudgment decision applicable to video playback quality optimization based on the mobile network handover information and the real-time video buffer length, and if so, rejecting the misjudgment decision and transmitting the next video segment to the client at the current video segment bitrate corresponding to the client, includes: Based on the switching timestamp of the current mobile network switching information, determine whether a mobile network switching event has occurred after the client generates the previous ABR decision. If so, obtain the preset detection result of the impact of mobile switching on video playback quality. Based on the detection results of the impact of mobile handover on video playback quality, determine whether the handover type of the current mobile network handover information belongs to the network capability optimization type. If the switching type belongs to the network capability optimization type, then determine whether the length of the real-time video buffer is equal to or greater than the duration threshold. If the length of the instant video buffer is equal to or greater than the duration threshold, the current ABR decision is determined to be a misjudgment decision applicable to video playback quality optimization, and the misjudgment decision is rejected and the next video segment is transmitted to the client at the current video segment bitrate corresponding to the client. The network capability optimization types include: 5G to 5G, 4G to 5G, and 4G to 4G.

6. A video playback quality measurement and optimization system under a mobile 5G network, characterized in that, include: A server is configured to execute the video ABR method based on 5G mobile information openness as described in any one of claims 1 to 4; The experimental bed device is mounted on a mobile carrier and is connected to the server via a mobile 5G network. The experimental bed device is used to record different types of video application metrics and collect the interaction signaling between itself and the 5G base station in real time when it switches. Based on the interaction signaling and video stream events, time alignment and qualitative analysis are performed to generate corresponding mobile switching impact detection results on video playback quality, and the mobile switching impact detection results are sent to the server.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the video ABR method based on 5G mobile information open as described in any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the video ABR method based on 5G mobile information open as described in any one of claims 1 to 4.

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