Method and device for realizing adaptive acceleration of video streaming of articulated naturality web based on SDN (Software Defined Network) controller

Abstract

The invention discloses an articulated naturality web video stream adaptive acceleration method and device based on an SDN (Software Defined Network) controller, and the method comprises the steps: carrying out the protocol recognition and multi-dimensional video stream feature extraction of video stream data flowing through a network key node through the SDN controller; according to the multi-dimensional video stream features, the network state and a preset transcoding strategy, intelligently deciding whether acceleration is needed and an acceleration mode is needed; if acceleration is needed, dynamic service chain instantiation is realized through the SDN controller, and the video stream is guided to the service chain through an SDN stream table; completing format conversion and code rate adaptation of the video stream in the service chain, and outputting a standardized video stream; edge caching is carried out on the video stream subjected to high-frequency access; and re-injecting the accelerated video stream into the original transmission path, and sending the accelerated video stream to a target client. According to the method and the device, non-inductive acceleration and intelligent optimization of the heterogeneous video stream in the articulated naturality web are realized.

Description

Technical Field

[0001] This invention relates to the field of video stream acceleration technology on the network side of the video network, and in particular to a method and apparatus for video stream adaptation and acceleration based on an SDN (Software-Defined Networking) controller. Background Technology

[0002] With the advancement of Video Internet of Things (Video IoT) technology, government departments at all levels (such as public security, transportation, and emergency response) have built numerous independently operating video surveillance systems. These systems are often built by different vendors, employing different video encoding standards (such as H.264, H.265, and AV1), transmission protocols (such as RTSP, GB / T28181, and ONVIF), and bitrate strategies, forming "video information silos." However, due to incompatible transmission protocols, mismatched bitrates, and high network latency, cross-system video calls often experience stuttering, screen tearing, delays of several seconds, or even playback failures, affecting emergency response efficiency and decision-making quality. Summary of the Invention

[0003] To address the aforementioned technical issues, this invention provides a method and apparatus for accelerating video stream adaptation in a video network based on an SDN controller. This method enables low-latency and high-definition sharing of video streams across systems without altering existing cameras and monitoring platforms. It supports automatic adaptation to mainstream encoding formats, dynamic bitrate adjustment, and possesses excellent scalability and security.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In one embodiment of the present invention, a method for accelerating video stream adaptation in a video network based on an SDN controller is proposed, the method comprising:

[0006] The SDN controller performs protocol identification and multi-dimensional video stream feature extraction on video stream data flowing through key network nodes.

[0007] The SDN controller intelligently decides whether acceleration is needed and how to accelerate based on the multi-dimensional video stream characteristics, network status, and preset transcoding strategy. If acceleration is needed, the SDN controller instantiates a dynamic service chain and guides the video stream to the service chain through the SDN flow table. The service chain performs format conversion and bitrate adaptation of the video stream and outputs a standardized video stream.

[0008] The SDN controller performs edge caching on the frequently accessed video stream;

[0009] The SDN controller re-injects the accelerated video stream into the original transmission path and delivers it to the target client.

[0010] Furthermore, the preset transcoding strategy is as follows:

[0011] If the terminal does not support H.265, but the video stream is H.265 encoded, then transcoding will be triggered;

[0012] If the link bandwidth is less than 1.2 times the video bitrate, then bitrate adaptation is triggered.

[0013] If it is a mobile terminal in a high packet loss environment, forward error correction enhancement will be triggered;

[0014] For live streams that are accessed frequently, enable edge caching preloading.

[0015] Furthermore, the triggering conditions for the edge cache are as follows:

[0016] The video stream is marked as frequently accessed;

[0017] The video stream content is either on-demand or a segmented live stream.

[0018] Furthermore, the intelligent optimization mechanism of the edge cache is as follows:

[0019] Employ the least frequently used or weighted elimination algorithm based on access frequency;

[0020] Based on program schedules or user behavior predictions, cache upcoming content in advance;

[0021] It supports a cache expiration notification mechanism, which automatically clears the old cache when the source content is updated.

[0022] In one embodiment of the present invention, a video stream adaptation and acceleration device based on an SDN controller for video networks is also proposed, the device comprising:

[0023] The video stream identification module is used to perform protocol identification and multi-dimensional video stream feature extraction on video stream data flowing through key network nodes through the SDN controller;

[0024] The strategy decision execution module is used to intelligently decide whether acceleration is needed and the acceleration method based on the multi-dimensional video stream characteristics, network status and preset transcoding strategy through the SDN controller.

[0025] The link construction module is used to realize the dynamic service chain instantiation through the SDN controller and guide the video stream to the service chain through the SDN flow table;

[0026] The transmission processing module is used to complete the format conversion and bitrate adaptation of the video stream in the service chain and output a standardized video stream;

[0027] A cache optimization module is used to perform edge caching on the frequently accessed video stream through the SDN controller;

[0028] The transmission module is used to re-inject the accelerated video stream into the original transmission path via the SDN controller and deliver it to the target client.

[0029] Furthermore, the edge cache is deployed at edge nodes or regional aggregation centers, close to the user side.

[0030] Furthermore, the video stream re-injection point is selected from network nodes that are close to the target client.

[0031] In one embodiment of the present invention, a computer device is also proposed, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned acceleration of video stream adaptation based on the SDN controller for video networks.

[0032] In one embodiment of the present invention, a computer-readable storage medium is also proposed, which stores a computer program that executes a video stream adaptation acceleration based on an SDN controller.

[0033] Beneficial effects:

[0034] 1. Propose the “Transparent Acceleration Chain” mechanism: For the first time, the advantages of SDN are used to accelerate video streams on the video network without any noticeable difference, realizing intelligent optimization on the network side without requiring terminal adaptation.

[0035] 2. Automatic cross-protocol and cross-bitrate adaptation: Supports recognition and conversion of mainstream encoding formats, solving the problem of interoperability between heterogeneous systems.

[0036] 3. Edge caching and prefetching optimization: By combining access frequency and spatiotemporal characteristics, edge caching of high-frequency videos is implemented, significantly reducing back-to-origin bandwidth.

[0037] 4. Centralized control and policy-driven: The SDN controller provides a global view and intelligent scheduling, and supports QoS classification and security policy embedding.

[0038] 5. High scalability: Supports plug-in expansion and can integrate AI enhancement and other functional modules in the future. Attached Figure Description

[0039] Figure 1 This is a flowchart of a method for accelerating video stream adaptation in a video network based on an SDN controller, according to an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the structure of the video stream adaptation and acceleration device for the video network based on the SDN controller of the present invention;

[0041] Figure 3 This is a schematic diagram of the computer device structure of the present invention. Detailed Implementation

[0042] The principles and spirit of the present invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0043] Those skilled in the art will recognize that embodiments of the present invention can be implemented as an apparatus, device, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0044] According to an embodiment of the present invention, a method for video stream adaptation and acceleration in video networks based on an SDN controller is proposed. By deeply integrating the SDN controller with network functions, an adaptation and controller mechanism with cross-vendor terminals and cross-protocol and cross-bitrate adaptation capabilities is constructed to achieve seamless acceleration and intelligent optimization of heterogeneous video streams in video networks.

[0045] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0046] This invention proposes a method for accelerating video stream adaptation in video networks based on an SDN controller. The core idea is to construct a manageable and controllable service chain on the network side. During video stream transmission, the SDN controller automatically identifies video stream characteristics and deploys lightweight decoding, transcoding, and caching functions as needed, achieving seamless optimization of the video stream. Key steps are as follows:

[0047] 1. Video Stream Identification: The SDN controller performs protocol identification and multi-dimensional video stream feature extraction on video stream data flowing through key network nodes, including information such as encoding format, resolution, bitrate, and source / destination address;

[0048] 2. Strategy Decision Execution: The SDN controller intelligently decides whether acceleration is needed and the acceleration method based on the multi-dimensional video stream characteristics, network status, and preset transcoding strategies (such as target terminal capabilities, network bandwidth, and QoS level).

[0049] 3. Constructing the link: If acceleration is required, the SDN controller is used to instantiate the dynamic service chain, and the video stream is guided to the service chain through the SDN flow table;

[0050] 4. Transmission Processing: The video stream is converted into a standardized video stream by performing format conversion and bitrate adaptation within the service chain.

[0051] 5. Cache optimization: The SDN controller performs edge caching on the frequently accessed video streams, so that subsequent requests can be directly retrieved from the edge cache, reducing the need to retrieve data from the origin server.

[0052] 6. Complete transmission: The accelerated video stream is re-injected into the original transmission path through the SDN controller and delivered to the target client.

[0053] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0054] To provide a clearer explanation of the above-mentioned video stream adaptation acceleration based on SDN controller, a specific embodiment will be used for illustration below. However, it is worth noting that this embodiment is only for better illustrating the present invention and does not constitute an improper limitation of the present invention.

[0055] Example:

[0056] This invention proposes a centralized control capability for Software-Defined Networking (SDN) to achieve intelligent identification, dynamic acceleration, and transparent processing of video streams. The entire device performs seamless optimization of the video stream without altering the behavior of the terminal devices, significantly improving video playback quality and network resource utilization. The overall workflow is as follows: Figure 1 As shown.

[0057] The technical solution will be explained in detail below from six core steps:

[0058] 1. SDN controller based on multi-dimensional video stream feature extraction from video stream data

[0059] (1) Integrate a Deep Packet Inspection (DPI) module into the SDN controller and deploy it at key network nodes (such as regional aggregation layer or core layer) to perform real-time mirror analysis on the passing video stream data.

[0060] (2) Protocol identification: Supports the identification of mainstream video transmission protocols, including RTSP, RTMP, HLS, DASH, SRT and WebRTC, etc., and matches them through the protocol feature type in the SDN controller.

[0061] (3) Multidimensional video stream feature extraction:

[0062] Encoding format: By analyzing the NALU header information or RTP payload type of the video stream, encoding standards such as H.264, H.265, AV1, and VP9 can be identified;

[0063] Resolution and frame rate: Parse SDP negotiation information or video stream metadata to obtain resolution (e.g., 1080p, 4K) and frame rate (30fps, 60fps).

[0064] Bitrate: The instantaneous bitrate is calculated based on the data packet volume per unit time, and the average bitrate is calculated by combining the data packet volume per unit time.

[0065] Source / Destination Address: Extract the IP 5-tuple (source / destination IP, port, and protocol) to locate the sending end (camera or streaming media server) and receiving end (client or monitoring platform) of the video stream.

[0066] QoS Marker: Identifies the DSCP or 802.1P field in the IP header to determine the priority level of the current video stream.

[0067] Camera A (H.265 encoding, 8Mbps bitrate) sends a video stream request to monitoring platform B via the RTSP protocol;

[0068] The video stream enters the SDN network through the access switch.

[0069] from scapy.all import *

[0070] def detect_video_codec(packet):

[0071] if packet.haslayer(RTP):

[0072] payload = packet[RTP].payload

[0073] if len(payload) > 4:

[0074] # H.264 NALU Header Recognition

[0075] if payload[0] == 0x67 or payload[0] == 0x68:

[0076] return "h264"

[0077] # H.265 VPS / SPS identification

[0078] elif payload[0] == 0x40 or payload[0] == 0x42:

[0079] return "h265"

[0080] return "unknown"

[0081] The SDN switch mirrors the first packet of the video stream to the SDN controller;

[0082] The SDN controller calls the DPI module to analyze the RTP / RTCP packet header, identifies the encoding format as H.265, the bitrate as 8Mbps, and the target client as mobile terminal C (only H.264 is supported, with a maximum bitrate of 2Mbps).

[0083] The SDN controller presets a transcoding strategy and determines whether transcoding acceleration is needed.

[0084] import requests

[0085] def deploy_acceleration_chain(nfv_manager_ip, chain_config):

[0086] url = f"http: / / {nfv_manager_ip} / api / v1 / acceleration-chains"

[0087] headers = {"Content-Type": "application / json"}

[0088] response = requests.post(url, json=chain_config, headers=headers)

[0089] return response.json()

[0090] 2. Intelligent Accelerated Judgment Mechanism Based on Multi-Dimensional Strategies

[0091] Based on the characteristics of the multi-dimensional video stream, network status, and preset transcoding strategies, the system intelligently decides whether acceleration is needed and what acceleration method to use.

[0092] (1) Terminal capabilities: Obtain the target client's decoding capabilities (whether it supports H.265), screen resolution, and network access method (4G / 5G / Wi-Fi) through terminal registration information or active probing (such as STUN / ICE).

[0093] (2) Network status: Obtain the bandwidth, latency and packet loss rate of the current path (from the topology-aware module of the SDN controller).

[0094] (3) QoS level: Set priority policies according to service type, such as public security monitoring (QoS=5) taking precedence over ordinary park monitoring (QoS=2);

[0095] (4) Policy rule base: The preset transcoding policies are as follows:

[0096] If the terminal does not support H.265 but the video stream is H.265 encoded, then transcoding will be triggered;

[0097] If the link bandwidth is less than 1.2 times the video bitrate, then bitrate adaptation is triggered.

[0098] If it is a mobile terminal in a high packet loss environment, forward error correction (FEC) enhancement is triggered;

[0099] If the live stream is accessed frequently, enable edge caching preloading.

[0100] (5) Decision-making process:

[0101] The SDN controller pre-defines transcoding strategies based on multi-dimensional video stream characteristics and network status.

[0102] The strategy engine matches the strategy rule base to generate rules that accelerate decision-making.

[0103] No acceleration required: Video stream is directly passed through;

[0104] Transcoding required: The transcoding function in the SDN controller is called to perform format conversion with the preset transcoding strategy;

[0105] Bitrate adaptation required: Dynamic bitrate compression should be performed;

[0106] Cache-accelerated: Mark as cacheable stream and trigger prefetching;

[0107] The decision results include: whether to accelerate, acceleration type, target output format (e.g., H.264@1080p@2Mbps) and VNF chain template ID.

[0108] 3. SDN Controller Dynamic Service Chain Instantiation and Path Guidance

[0109] Objective: To dynamically create and deploy a ServiceFunction Chain (SFC) based on acceleration requirements and a pre-defined transcoding strategy, and to precisely guide the video stream to the chain.

[0110] Implementation mechanism:

[0111] (1) Transcoding implementation module in SDN controller:

[0112] vDecoder: A software decoder (supports H.264 / H.265 / AV1);

[0113] vTranscoder: Real-time transcoder (FFmpeg + GPU acceleration);

[0114] vCache: Edge cache node (based on Redis or Nginx cache);

[0115] vFEC: Forward Error Correction Module;

[0116] vRateLimiter: Bitrate shaper.

[0117] (2) Service chain example:

[0118] Path A (transcoding chain): vDecoder→vTranscoder→vCache;

[0119] Path B (Cache Acceleration Chain): vCache → vRateLimiter.

[0120] Dynamic service chain instantiation process:

[0121] Resources can be instantiated on edge or regional nodes using orchestration capabilities in an SDN controller (such as OpenStack+Tacker or Kubernetes);

[0122] Allocate computing resources (CPU, GPU, memory) and network interfaces;

[0123] Start the virtual service chain and complete the internal connection configuration.

[0124] SDN Flow Table Guidance:

[0125] (3) The SDN controller generates OpenFlow flow table entries (SDN flow table guidance is the key to achieving precise control of data flow. The core logic is that the SDN controller generates OpenFlow flow table entries. The SDN controller interacts with network devices through the OpenFlow protocol in the forwarding plane of the SDN control, obtains information such as the current network topology, device port status and link bandwidth in real time, and confirms the reachability of the virtual service chain entry and the corresponding forwarding path), and redirects the matched video stream to the newly created service chain entry.

[0126] Note: The "forwarding rule list" issued by the SDN controller to network devices (such as switches) is based on the OpenFlow protocol. Its core consists of "matching fields" and "action fields." Matching fields are used to identify specific data (such as the video stream in this article, which can be matched by IP, port, and protocol). Action fields define how the matched data is processed (such as forwarding to the service chain ingress, discarding, or modifying fields). The SDN controller manages the SDN flow tables to flexibly control the direction of video stream data flow without modifying the network device hardware.

[0127] 4. Seamless format conversion and bitrate adaptation of video streams

[0128] The service chain completes operations such as video stream decoding, transcoding, and bitrate adaptation, outputting a standardized video stream adapted to the terminal's capabilities, all transparent to the terminal.

[0129] Processing flow:

[0130] (1) Decoding: vDecoder decodes the raw video stream (such as H.265 4K) into raw YUV frames;

[0131] (2) Transcoding / Bitrate Adaptation:

[0132] vTranscoder uses FFmpeg (short for Fast Forward MPEG, an open-source audio and video codec toolkit that supports conversion and processing of various audio and video formats) + GPU acceleration for real-time transcoding.

[0133] The target format is set according to the policy decision, such as converting to H.264 1080p 2Mbps;

[0134] Supports Dynamic Bitrate Control (ABR): Adjusts the output bitrate in real time based on network fluctuations;

[0135] (3) Encapsulation output: Re-encapsulate into RTMP or HLS format, retaining the original timestamp and synchronization information.

[0136] Output example:

[0137] Input stream: H.265@4K@8Mbps → Output stream: H.264@1080p@2Mbps;

[0138] Suitable for bandwidth-constrained mobile clients or older decoding devices.

[0139] Stream guidance and transparent processing:

[0140] The SDN controller issues OpenFlow flow tables;

[0141] The video stream is directed to the service chain;

[0142] Decode H.265 to YUV using the lightweight FFmpeg library;

[0143] The video encoding library is used to transcode H.264, with the bitrate controlled at 2Mbps;

[0144] The key I-frame (Intra-coded Picture, also known as a key frame) and P-frame (Predictive-coded Picture, also known as a prediction frame, cannot be decoded independently and needs to be predicted and calculated based on the content of the previous I-frame or P-frame; it only stores the difference information with the reference frame, has a higher compression ratio, and can effectively reduce the storage space and transmission bandwidth of the video stream) are stored in the Redis cache, with TTL set to 300s.

[0145] from ryu.base import app_manager

[0146] from ryu.controller import ofp_event

[0147] from ryu.ofproto import ofproto_v1_3

[0148] class TransparentAccelerator(app_manager.RyuApp):

[0149] OFP_VERSIONS = [ofproto_v1_3.OFP_VERSION]

[0150] def __init__(self, *args, **kwargs):

[0151] super(TransparentAccelerator, self).__init__(*args, **kwargs)

[0152] self.mac_to_port = {}

[0153] @set_ev_cls(ofp_event.EventOFPPacketIn, MAIN_DISPATCHER)

[0154] def packet_in_handler(self, ev):

[0155] msg = ev.msg

[0156] datapath = msg.datapath

[0157] ofproto = datapath.ofproto

[0158] parser = datapath.ofproto_parser

[0159] # Parse the packet header and identify the video stream

[0160] pkt = packet.Packet(msg.data)

[0161] codec = detect_video_codec(pkt)

[0162] If codec is in ["h264", "h265"]:

[0163] # Deploy the acceleration chain and bootstrap

[0164] chain_id = deploy_acceleration_chain("192.168.10.100", {

[0165] "vnfs": [{"type": "transcoder", "dst_format": "h264"}]

[0166] })

[0167] # Deploy flow tables to the acceleration chain

[0168] out_port = self.get_acceleration_port(chain_id)

[0169] actions = [parser.OFPActionOutput(out_port)]

[0170] self.add_flow(datapath, 1000, match, actions)

[0171] 5. High-frequency video content acceleration mechanism based on edge caching

[0172] Objective: To intelligently cache frequently accessed video stream content, reduce repeated transmissions and pressure on the origin server, and achieve "process once, reuse multiple times".

[0173] Caching strategy:

[0174] (1) Cache triggering conditions:

[0175] The video stream is marked as "high-frequency access" (such as live streams and popular replays);

[0176] The content is either video-on-demand (such as HLS TS segments) or segmented live stream;

[0177] (2) Cache granularity:

[0178] Live stream: Keyframes are cached in units of GOP (Group of Pictures);

[0179] On-demand streaming: caching the entire video or in segments (e.g., every 10 seconds);

[0180] (3) Cache location: Deployed at edge nodes or regional aggregation centers, close to the user side.

[0181] Caching process:

[0182] After the video stream is processed by the service chain, the vCache module determines whether the cache triggering conditions are met.

[0183] If the conditions are met, the processed standardized video clips will be written to the local SSD cache.

[0184] Establish a "URL→cached path" mapping index to support fast lookup.

[0185] Cache hits and responses:

[0186] (1) When a new request arrives, the SDN controller first queries the cache index;

[0187] (2) If a hit occurs, the cached content is returned directly through the vCache module, bypassing the source server and the transcoding chain;

[0188] (3) If a miss occurs, proceed with the normal acceleration process and add the result to the cache.

[0189] Intelligent optimization mechanism:

[0190] (1) Cache eviction strategy: Use LFU (Least Frequently Used) or a weighted eviction algorithm based on access frequency;

[0191] (2) Preloading mechanism: Based on the program schedule or user behavior prediction, cache the content to be accessed in advance;

[0192] (3) Consistency guarantee: Supports cache expiration notification mechanism, automatically clearing old cache when source content is updated.

[0193] 6. Transparent re-injection of processed video streams and seamless delivery to the terminal

[0194] The accelerated video stream is securely and reliably reinjected into the original transmission path, ensuring accurate delivery to the target client, and the entire process is completely transparent to the terminal.

[0195] Implementation process:

[0196] Video stream re-injection point selection:

[0197] (1) Select a network node close to the target client as the injection point (such as an edge switch or access router).

[0198] (2) Avoid cross-core network back-injection to reduce additional latency.

[0199] Flow table rewriting and path restoration:

[0200] (1) The SDN controller issues an SDN flow table to guide the processed video stream from the VNF chain exit to the original destination address;

[0201] (2) Modify IP header information (such as TTL, checksum) to ensure compliance;

[0202] (3) If NAT or load balancing is used, the session table entries need to be updated.

[0203] Session persistence:

[0204] (1) Maintain the original TCP / RTMP session state to avoid connection interruption;

[0205] (2) For UDP streams (such as RTP), continuity is ensured by sequence number and timestamp.

[0206] QoS tag recovery:

[0207] (1) Retain or re-mark DSCP values ​​to ensure that high-priority flows are given priority scheduling in the network.

[0208] Integrity verification:

[0209] (1) Perform CRC or MD5 verification before re-injection to prevent data corruption;

[0210] (2) Discard abnormal frames to avoid affecting playback quality.

[0211] (3) Use a path or downgrade to pass-through mode;

[0212] (4) Record logs and trigger alarms for operation and maintenance analysis.

[0213] Final delivery result:

[0214] (1) The video stream received by the client has a compatible format, reasonable bitrate, and smooth playback;

[0215] (2) No lag, no screen tearing, no interruptions;

[0216] (3) Users do not need to perform any additional operations, and the experience is seamlessly optimized.

[0217] Based on the same inventive concept, this invention also proposes a video stream adaptation and acceleration device for video networks based on an SDN controller. The implementation of this device can refer to the implementation of the method described above, and repeated details will not be repeated. The term "module" used below can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0218] Figure 2 This is a schematic diagram of the structure of the video stream adaptation and acceleration device for the video network based on the SDN controller of the present invention. Figure 2 As shown, the device includes:

[0219] The video stream identification module 101 is used to perform protocol identification and multi-dimensional video stream feature extraction on video stream data flowing through key network nodes through the SDN controller.

[0220] The strategy decision execution module 102 is used to intelligently decide whether acceleration is needed and the acceleration method based on the multi-dimensional video stream characteristics, network status and preset transcoding strategy through the SDN controller.

[0221] The link construction module 103 is used to instantiate the dynamic service chain through the SDN controller and guide the video stream to the service chain through the SDN flow table.

[0222] The transmission processing module 104 is used to complete the format conversion and bitrate adaptation of the video stream in the service chain and output a standardized video stream.

[0223] The cache optimization module 105 is used to perform edge caching on the frequently accessed video stream through the SDN controller; the edge cache is deployed at the edge node or regional aggregation center, close to the user side.

[0224] The transmission module 106 is used to re-inject the accelerated video stream into the original transmission path through the SDN controller and deliver it to the target client; the video stream re-injection point is selected from the network node closest to the target client.

[0225] It should be noted that although several modules for implementing a video stream adaptation and acceleration device based on an SDN controller have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules described above can be embodied in a single module. Conversely, the features and functions of a single module described above can be further divided and embodied by multiple modules.

[0226] Based on the aforementioned inventive concept, such as Figure 3 As shown, the present invention also proposes a computer device 200, including a memory 210, a processor 220, and a computer program 230 stored in the memory 210 and executable on the processor 220. When the processor 220 executes the computer program 230, it realizes the aforementioned video stream adaptation acceleration based on the SDN controller.

[0227] Based on the aforementioned inventive concept, the present invention also proposes a computer-readable storage medium storing a computer program that executes the aforementioned video stream adaptation acceleration based on an SDN controller for video networks.

[0228] The present invention proposes a method and apparatus for accelerating video stream adaptation in video networks based on an SDN controller, which has the following advantages:

[0229] 1. Propose the “Transparent Acceleration Chain” mechanism: For the first time, the advantages of SDN are used to accelerate video streams on the video network without any noticeable difference, realizing intelligent optimization on the network side without requiring terminal adaptation.

[0230] 2. Automatic cross-protocol and cross-bitrate adaptation: Supports recognition and conversion of mainstream encoding formats, solving the problem of interoperability between heterogeneous systems.

[0231] 3. Edge caching and prefetching optimization: By combining access frequency and spatiotemporal characteristics, edge caching of high-frequency videos is implemented, significantly reducing back-to-origin bandwidth.

[0232] 4. Centralized control and policy-driven: The SDN controller provides a global view and intelligent scheduling, and supports QoS classification and security policy embedding.

[0233] 5. High scalability: Supports plug-in expansion and can integrate AI enhancement and other functional modules in the future.

[0234] While the spirit and principles of the invention have been described with reference to several specific embodiments, it should be understood that the invention is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for ease of description. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

[0235] Regarding the limitation of the scope of protection of this invention, those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solution of this invention are still within the scope of protection of this invention.

Claims

1. A method for accelerating video stream adaptation in a video network based on an SDN controller, characterized in that, The method includes: The SDN controller performs protocol identification and multi-dimensional video stream feature extraction on video stream data flowing through key network nodes. The SDN controller intelligently decides whether acceleration is needed and how to accelerate based on the multi-dimensional video stream characteristics, network status, and preset transcoding strategy. If acceleration is needed, the SDN controller instantiates a dynamic service chain and guides the video stream to the service chain through the SDN flow table. The service chain performs format conversion and bitrate adaptation of the video stream and outputs a standardized video stream. The SDN controller performs edge caching on the frequently accessed video stream; The SDN controller re-injects the accelerated video stream into the original transmission path and delivers it to the target client.

2. The method for accelerating video stream adaptation in a video network based on an SDN controller according to claim 1, characterized in that, The preset transcoding strategy is as follows: If the terminal does not support H.265, but the video stream is H.265 encoded, then transcoding will be triggered; If the link bandwidth is less than 1.2 times the video bitrate, then bitrate adaptation is triggered. If it is a mobile terminal in a high packet loss environment, forward error correction enhancement will be triggered; For live streams that are accessed frequently, enable edge caching preloading.

3. The method for accelerating video stream adaptation in a video network based on an SDN controller according to claim 1, characterized in that, The triggering conditions for the edge cache are as follows: The video stream is marked as frequently accessed; The video stream content is either on-demand or a segmented live stream.

4. The method for accelerating video stream adaptation in a video network based on an SDN controller according to claim 1, characterized in that, The intelligent optimization mechanism of the edge cache is as follows: Employ the least frequently used or weighted elimination algorithm based on access frequency; Based on program schedules or user behavior predictions, cache upcoming content in advance; It supports a cache expiration notification mechanism, which automatically clears the old cache when the source content is updated.

5. A video stream adaptation and acceleration device for video networks based on an SDN controller, characterized in that, The device includes: The video stream identification module is used to perform protocol identification and multi-dimensional video stream feature extraction on video stream data flowing through key network nodes through the SDN controller; The strategy decision execution module is used to intelligently decide whether acceleration is needed and the acceleration method based on the multi-dimensional video stream characteristics, network status and preset transcoding strategy through the SDN controller. The link construction module is used to realize dynamic service chain instantiation through the SDN controller and guide the video stream to the service chain through the SDN flow table; The transmission processing module is used to complete the format conversion and bitrate adaptation of the video stream in the service chain and output a standardized video stream; A cache optimization module is used to perform edge caching on the frequently accessed video stream through the SDN controller; The transmission module is used to re-inject the accelerated video stream into the original transmission path via the SDN controller and deliver it to the target client.

6. The video stream adaptation and acceleration device based on an SDN controller for video networks according to claim 5, characterized in that, The edge cache is deployed at edge nodes or regional aggregation centers, close to the user side.

7. The video stream adaptation and acceleration device based on an SDN controller for video networks according to claim 5, characterized in that, The video stream re-injection point is selected from network nodes that are close to the target client.

8. A computer 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 method according to any one of claims 1-4.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs the method according to any one of claims 1-4.

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