Render node video stream low latency compositing output system

Abstract

This invention relates to the field of video stream compositing technology and discloses a low-latency compositing output system for video streams from rendering nodes. The system's input proxy module captures video stream data packets from multiple rendering nodes, parses and generates metadata tags in real time, and writes them to a shared memory queue. A frame synchronization manager dynamically connects to the shared memory queue, establishes a cross-node frame synchronization index table based on the timestamp sequence of the metadata tags, and triggers a frame interpolation command when consecutive timestamps are missing. A frame buffer allocator responds to the frame interpolation command by generating virtual frame placeholders and simultaneously allocates physical buffer addresses for video stream data packets with complete timestamp sequences, generating a frame storage mapping table. A compositing decision engine periodically polls this table, calculates frame space weights based on the topological location of the rendering nodes, and generates a distributed compositing path strategy based on the physical distribution of the buffer addresses. Through the collaboration of these modules, low-latency compositing output of video streams from multiple rendering nodes is achieved.

Description

Technical Field

[0001] This invention relates to the field of video stream compositing technology, specifically a low-latency video stream compositing and output system for rendering nodes. Background Technology

[0002] In scenarios such as real-time rendering, virtual production, and remote collaboration, the collaborative work of multiple rendering nodes has become a common method for generating complex visuals. In these scenarios, different rendering nodes need to process local image data simultaneously and transmit the generated video streams in real time to the compositing node for integration, ultimately outputting a complete dynamic image. However, efficient compositing of video streams faces many practical challenges during the distributed transmission and processing across multiple nodes.

[0003] When video stream data packets are transmitted across nodes, factors such as network fluctuations and differences in node computing load often cause timestamps to deviate upon arrival at the compositing end, and even lead to the loss of consecutive data packets. Current processing methods mostly limit data packet parsing to basic format verification, lacking real-time extraction and tagging of metadata. This makes it difficult to quickly locate the temporal correlation between video streams at each node during subsequent synchronization, easily causing misalignment in image stitching.

[0004] Frame synchronization mechanisms often rely on a preset time base and cannot dynamically adapt to scenarios such as increases or decreases in the number of nodes or changes in the transmission path. When a missing timestamp is detected, the common approach is to simply discard it or wait for retransmission. The former causes a jump in the image, while the latter increases latency, neither of which can meet the requirements for low-latency compositing.

[0005] Cache allocation strategies often focus on allocating physical storage resources without considering the impact of missing frames on cache space usage. When some frames fail to arrive on time due to transmission issues, the reserved physical cache space remains idle for an extended period, while new frames require additional resources, resulting in low cache utilization and indirectly increasing the complexity of data scheduling.

[0006] The planning of synthesis paths is often based on fixed node topology, ignoring the real-time computational load, cache location, and other physical distribution characteristics of each node. This can cause the transmission path of synthesized instructions to detour around nodes with high loads or choose cache addresses that are far away, further increasing the link time for data processing. Summary of the Invention

[0007] The purpose of this invention is to provide a low-latency video stream synthesis and output system for rendering nodes, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a low-latency video stream synthesis and output system for rendering nodes, the system comprising:

[0009] The input proxy module is used to capture video stream data packets transmitted by multiple rendering nodes, parse the data packet header information in real time to generate metadata tags, and write the metadata tags into a shared memory queue.

[0010] The frame synchronization manager dynamically connects to the shared memory queue, establishes a cross-node frame synchronization index table based on the timestamp sequence of the metadata tags, and triggers a frame filling instruction when consecutive timestamps are detected to be missing.

[0011] The frame buffer allocator generates virtual frame placeholders in response to the frame interpolation instruction, and simultaneously allocates physical buffer addresses for video stream data packets with complete timestamp sequences, and generates a frame storage mapping table.

[0012] The compositing decision engine periodically polls the frame storage mapping table, calculates the frame space weights based on the topological location of the rendering nodes, and generates a distributed compositing path strategy by combining the physical distribution of cache addresses.

[0013] Preferably, the frame synchronization manager further includes:

[0014] The timing verification unit extracts metadata tags within a continuous time window from the shared memory queue and calculates the timestamp difference between adjacent tags to generate a timing discreteness matrix.

[0015] When the variance of the temporal discreteness matrix exceeds a preset fluctuation threshold, the index building unit activates a backtracking mechanism to re-align the timestamp sequence and outputs the updated cross-node frame synchronization index table to the physical cache allocator.

[0016] Preferably, the frame buffer allocator specifically performs the following:

[0017] The virtual frame generator, upon receiving the frame interpolation instruction, creates a virtual frame carrying empty pixel data and marks the virtual frame identifier and associated rendering node number in the frame storage mapping table.

[0018] The physical address allocator scans the cross-node frame synchronization index table to obtain the complete timestamp sequence, allocates exclusive memory blocks for each video stream data packet, and generates a set of physical cache addresses containing memory block pointers and timestamp binding relationships.

[0019] Preferably, the system further includes:

[0020] The frame assembly controller reads video stream data packets in batches from the physical cache address set, reassembles pixel data blocks according to the instruction order of the distributed synthesis path strategy, and pushes the reassembled data packets to the video synthesis pipeline.

[0021] The pipeline status monitor captures the processing delay parameters of the video compositing pipeline in real time and feeds back the delay parameters to the compositing decision engine to trigger path strategy updates.

[0022] Preferably, the synthetic decision engine includes:

[0023] The topology analysis unit parses the network coordinates and hardware configuration parameters of the rendering nodes to generate a three-dimensional spatial distribution model.

[0024] The weight calculation unit calculates the spatial influence coefficient of each rendering node frame by frame based on the node spacing and bandwidth capacity in the three-dimensional spatial distribution model.

[0025] The path generation unit dynamically constructs a synthetic data stream path that minimizes the number of transmission hops by combining the spatial influence coefficient with the physical cache address distribution in the frame storage mapping table.

[0026] Preferably, the frame assembly controller further includes:

[0027] The pixel verifier performs cross-node color space consistency checks during the reassembly of pixel data blocks and generates color correction instructions when a color gamut offset is detected.

[0028] The dynamic compensator adjusts the brightness mapping curve of the pixel data block in response to the color correction command and outputs a normalized pixel matrix to the video compositing pipeline.

[0029] Preferably, the system further includes:

[0030] The priority scheduler receives the latency parameters from the pipeline status monitor and the frame interpolation instructions from the frame synchronization manager, and calculates the real-time load priority of the rendering nodes.

[0031] The resource reallocator dynamically adjusts the generation frequency of virtual frame placeholders according to the real-time load priority and sends a memory block expansion request to the physical cache allocator.

[0032] Preferably, the video compositing pipeline includes:

[0033] The multi-layer blending unit receives the standardized pixel matrix and performs a transparency overlay operation to generate a blending layer cache.

[0034] A temporal filter performs inter-frame motion vector analysis on the hybrid layer buffer, compensates for temporal continuity parameters based on the analysis results, and outputs the final synthesized frame.

[0035] Preferably, the priority scheduler specifically operates as follows:

[0036] The load monitoring unit periodically collects the CPU utilization and network throughput of the rendering nodes;

[0037] The priority mapping unit inputs the CPU utilization and network throughput into the multidimensional decision model and outputs the real-time scheduling weight coefficients of each rendering node.

[0038] The instruction generation unit compares the real-time scheduling weight coefficient with the preset load threshold to generate a resource adjustment instruction set that includes the memory expansion ratio and the frame interpolation rate.

[0039] Preferably, the system further includes:

[0040] The output compression channel receives the final synthesized frame and performs dynamic bitrate encoding based on motion vectors to write the compressed data stream into the network transmission buffer.

[0041] The buffer controller monitors the filling status of the network transmission buffer and sends a frame downsizing command to the frame assembly controller when the filling amount exceeds the window threshold.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] The input proxy module captures and parses video stream data packets transmitted from multiple rendering nodes in real time, and the generated metadata tags are directly written to a shared memory queue, enabling the video stream information of each node to be accessed quickly in a unified metadata format. This processing method reduces the time spent on format conversion when data flows between different modules. At the same time, the key information such as timestamps contained in the metadata tags provides directly callable basic data for subsequent frame synchronization, making cross-node frame correlation analysis more targeted.

[0044] The frame synchronization manager dynamically connects to a shared memory queue and builds a cross-node frame synchronization index table based on the timestamp sequence of metadata tags, enabling video stream frames from different rendering nodes to form an ordered association along the time dimension. When consecutive timestamps are missing, a frame interpolation command is triggered, providing a timely response to data packet loss. This dynamic response mechanism avoids compositing stagnation caused by waiting for missing frames and reduces image breaks caused by skipping missing frames, ensuring the frame sequence remains continuous in the time dimension.

[0045] The frame buffer allocator generates virtual frame placeholders in response to frame interpolation commands, and simultaneously allocates physical buffer addresses for video stream data packets with complete timestamp sequences. The resulting frame storage mapping table takes into account the storage needs of both missing and complete frames. The introduction of virtual frame placeholders avoids idle buffer space caused by missing frames, allowing for more flexible scheduling of buffer resources. Meanwhile, the precise allocation of physical buffer addresses ensures that complete frame data can be efficiently stored and retrieved, reducing the time spent searching for data in the buffer and improving the overall utilization efficiency of buffer resources.

[0046] The compositing decision engine periodically polls the frame storage mapping table and calculates frame space weights based on the topological location of the rendering nodes. This allows the spatial location characteristics of each node to be quantified as a reference factor for compositing path planning. A distributed compositing path strategy is generated by combining the physical distribution of cache addresses, enabling the transmission path of compositing instructions to adapt to the real-time node locations and cache distribution status. This dynamic planning approach avoids redundant transmission caused by fixed paths, making the flow of data between nodes more closely match the actual physical layout, shortening the execution chain of compositing instructions, and reducing unnecessary time consumption.

[0047] The modules work collaboratively, enabling a seamless processing chain for video streams from multiple rendering nodes across transmission, synchronization, storage, and compositing. Real-time metadata processing provides precise data for synchronization, a dynamic synchronization mechanism ensures the temporal continuity of frame sequences, optimized cache allocation improves resource utilization efficiency, and an adaptive compositing path shortens data transfer time. Overall, this allows the composited output of multi-rendering node video streams to maintain low latency and good image continuity even when facing network fluctuations and node changes. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating the working principle of the low-latency video stream synthesis and output system for rendering nodes described in this invention.

[0049] Figure 2 This is a schematic diagram illustrating the working principle of a frame synchronization manager.

[0050] Figure 3 This is a diagram illustrating the working principle of a synthetic decision engine.

[0051] Figure 4 This is a detailed diagram illustrating the working principle of a priority scheduler. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 This invention provides a low-latency video stream compositing and output system for rendering nodes, the system comprising:

[0054] The system comprises an input agent module, a frame synchronization manager, a frame buffer allocator, and a compositing decision engine. The input agent module captures UDP video stream packets from multiple rendering nodes via the network interface card (NIC) driver. Using zero-copy technology, it parses the packet header information into metadata tags containing timestamps, node IDs, and resolution parameters, and writes these tags to a circular shared memory queue through atomic operations. The frame synchronization manager connects to the shared memory queue via memory mapping, sorts the timestamp sequence using a red-black tree data structure, and establishes a cross-node frame synchronization index table containing the mapping relationship between frame sequence numbers and nodes. When a timestamp interval exceeds 33ms, it sends a frame interpolation command to the frame buffer allocator. Upon receiving the interpolation command, the frame buffer allocator uses pre-allocated blank memory areas to generate virtual frame placeholders and allocates 4KB-aligned physical buffer addresses for valid video stream packets, generating a frame storage mapping table containing virtual frame identifiers and physical address pointers. The compositing decision engine polls the frame storage mapping table every 10ms, calculates Euclidean distance weights based on the 3D spatial coordinates of the rendering nodes, and generates a cross-node compositing path strategy based on the memory distribution characteristics of the NUMA architecture.

[0055] Example 1: See Figure 2 The timing verification unit uses a sliding window mechanism to process metadata tags in the shared memory queue. The window size is fixed at 5 consecutive frames. During each processing iteration, the timestamp information of all tags within the current window is extracted, the time difference between adjacent frames is calculated, and an N×N discreteness matrix is ​​generated. Each element in the matrix represents the time interval variation of a specific rendering node between consecutive frames. The calculation of the discreteness matrix employs a lock-free concurrent access mechanism to avoid data race issues in a multi-threaded environment. When the overall variance of the matrix exceeds a preset fluctuation threshold of 15%, it indicates a clock synchronization deviation in some rendering nodes, triggering a clock backtracking mechanism based on the NTP protocol. The backtracking process first locks all metadata tags within the current window, then recalculates the reference clock of each node using a timestamp alignment algorithm, and finally generates a calibrated timestamp sequence.

[0056] The index building unit uses an improved B+ tree structure to store the cross-node frame synchronization index table. Internal nodes of the tree structure store timestamp range information, while leaf nodes contain bidirectional pointers to timestamps and physical memory addresses. The B+ tree construction process employs a batch insertion optimization strategy to reduce the performance overhead caused by tree structure adjustments. When updating the index table, it first checks if the timestamp to be inserted already exists. If it does, the corresponding physical address pointer is updated; otherwise, a new timestamp-address mapping is created in the leaf node. The index table supports multi-threaded concurrent queries and ensures data consistency through a read-write lock mechanism. When a calibration signal is received from the timing verification unit, the index building unit pauses all write operations, rebuilds the entire B+ tree, and notifies the frame buffer allocator to update the memory mapping after completion.

[0057] Upon receiving a frame completion instruction, the virtual frame generator first checks the timestamp range of the missing frames, then creates an empty frame in YUV420 format conforming to the standard resolution within a pre-allocated blank memory area. The pixel data of the empty frame is initialized to neutral gray and marked with a 0xFF virtual frame identifier in the frame header information. The virtual frame generation process employs memory pooling technology to avoid frequent memory allocation and deallocation operations. The generated virtual frames are associated with the original rendering node number, ensuring that the frame source can be correctly identified in subsequent compositing stages. The frame storage mapping table uses a hybrid structure of hash tables and doubly linked lists to store virtual frame information. The hash table is used for quickly finding virtual frames with a specific timestamp, while the doubly linked list maintains the temporal order of the virtual frames.

[0058] The physical address allocator is responsible for allocating contiguous DDR4 memory blocks for valid video stream data packets. The allocation strategy uses 4KB alignment to improve memory access efficiency. The allocation process first scans the cross-node frame synchronization index table to obtain all complete timestamp sequences, and then allocates an independent memory block for each data packet according to the timestamp order. The size of the memory blocks is dynamically adjusted to accommodate video stream data of different resolutions. After allocation, the physical address allocator generates a set of physical cache addresses containing the binding relationships between memory block pointers and timestamps. The address set uses a two-level index structure: the first-level index is sorted by timestamp for quick location of data in a specific frame; the second-level index is categorized by node ID, supporting batch data reading by rendering node.

[0059] The memory defragmentation module runs continuously during physical address allocation, monitoring the fragmentation level of the memory pool. When the fragmentation rate exceeds a threshold, the module initiates a compaction operation, reorganizing scattered memory blocks into contiguous space. The compaction process employs copy-on-write technology to avoid impacting ongoing video streaming operations. The expansion and contraction of the memory pool utilizes a dynamic link library loading mechanism, supporting runtime adjustment of the pool size to adapt to memory requirements under different load conditions. Updates to the physical cache address set use atomic variables to ensure thread safety and data consistency in a multi-threaded environment.

[0060] The calculation of the temporal discreteness matrix is ​​optimized using SIMD instructions to accelerate the batch processing of differences between adjacent timestamps. Variance detection of the matrix is ​​based on a moving average algorithm to avoid misjudgments caused by instantaneous fluctuations. When the temporal verification unit detects abnormal fluctuations, it sends a high-priority interrupt signal to the index building unit, triggering an immediate calibration process. During calibration, the system temporarily buffers newly arriving video stream data packets until the timestamp sequence is realigned. The calibrated timestamp sequence is then communicated to all relevant modules via a shared memory event channel to ensure system consistency.

[0061] The generation frequency of virtual frame placeholders is controlled by the dynamic load balancing module, which automatically adjusts the frame interpolation strategy based on the current system load. Under high load conditions, the virtual frame generator reduces the frame interpolation frequency to prioritize the processing efficiency of valid frames. The frame storage mapping table uses an LRU caching strategy to manage virtual frame data; virtual frames that have not been accessed for a long time are automatically reclaimed, releasing memory resources. The coordination between the physical address allocator and the virtual frame generator is achieved through a lock-free message queue, reducing latency caused by thread blocking.

[0062] Example 2: See Figure 3 The frame assembly controller reads video stream data packets in batches from the physical buffer address set via Direct Memory Access (DMA), employing zero-copy technology to reduce the number of times data is copied in memory. The read operation is based on a distributed path strategy generated by the compositing decision engine, which defines the priority order of data packets from different rendering nodes. The data packet reassembly process uses Single Instruction Multiple Data (SIMD) parallel processing technology to rearrange pixel blocks from multiple nodes according to their spatial location. The reassembly algorithm uses row-major storage format conversion to ensure that the pixel data layout in memory from different nodes conforms to the input requirements of the video compositing pipeline. The converted complete frame buffer is transmitted to the video compositing module via the PCIE Gen4 channel, and a data packet verification mechanism ensures data integrity during transmission.

[0063] The pipeline status monitor collects processing latency parameters of the video compositing pipeline in real time using hardware performance counters, with monitoring points distributed across various key stages of the pipeline. The collected data includes frame decoding time, pixel blending time, and final encoding latency, all recorded with high-precision timestamps. When the system detects that the overall processing latency exceeds a preset 8ms threshold, the status monitor generates a feedback data packet containing latency gradient information. This feedback data is compressed using differential coding and sent to the compositing decision engine via a dedicated channel. The feedback processing mechanism employs an event-driven architecture to ensure timely responses to latency anomalies.

[0064] The topology analysis unit collects network topology information of the rendering nodes through both active probing and passive listening. Active probing employs a customized network discovery protocol to obtain the physical location coordinates and network connectivity information of each node. Passive listening analyzes the switch hop count and link latency along the packet transmission path. After cleaning and normalization, the collected raw data is used to construct a spatial distribution model containing the three-dimensional coordinates of nodes, network latency, and bandwidth characteristics. The node location information in the model is represented using a relative coordinate system, with the origin set at the central node of the synthesized system. Network topology updates employ an incremental maintenance strategy, triggering model reconstruction only when a connection change is detected.

[0065] When processing the spatial distribution model, the weight calculation unit comprehensively considers both the physical distance between nodes and network quality. Distance calculation employs an improved Manhattan metric algorithm, which performs a weighted summation of the differences in the x, y, and z dimensions in three-dimensional space. Network quality factors include link bandwidth utilization and historical packet loss rate, which are obtained through sliding window statistics. The calculation process generates a dynamic influence coefficient between 0.1 and 1.0 for each rendering node, and the coefficient value is adjusted in real time according to network conditions. A smooth transition algorithm is used to update the influence coefficient to avoid unstable synthesis quality caused by drastic fluctuations.

[0066] The path generation unit combines weighting coefficients and memory distribution to construct the optimal synthetic path using graph theory algorithms. Path search is based on Steiner tree theory, minimizing the overall transmission hop count while considering node influence coefficients. The generated path policy includes a primary routing table and backup routing schemes, with the routing table detailing the order in which each data packet should traverse nodes. A fault detection mechanism continuously monitors path execution, automatically switching to a backup path when a node response times out or data verification fails. Path policy updates employ a version control mechanism to ensure all processing modules use consistent path information.

[0067] The distributed compositing path employs a pipelined parallel architecture, with overlapping data processing phases across different nodes. The system maintains an independent state machine for each rendering node, tracking its data processing progress. Synchronization between state machines is achieved through a barrier mechanism to ensure the orderly execution of critical operations. Data packets are transmitted between nodes using a reliable UDP protocol with flow control, balancing transmission efficiency and reliability. Adaptive bitrate adjustment technology is employed during transmission, dynamically adjusting the data transmission rate based on network conditions.

[0068] The synthetic decision engine's periodic polling mechanism employs a multi-level priority design, assigning different scheduling weights to different types of processing tasks. High-priority tasks, such as path policy updates, can preempt low-priority tasks, such as topology analysis. The task scheduler uses a time-slice round-robin algorithm to ensure that all types of tasks receive a fair processing opportunity. All parameters used in the decision-making process are stored as atomic variables, supporting concurrent access by multiple threads. The engine internally maintains a decision log, recording important operations and state changes for subsequent analysis and troubleshooting.

[0069] The video compositing pipeline employs a circular queue design for its input buffer, supporting asynchronous operations between producers and consumers. Buffer management utilizes a watermark control strategy, automatically triggering flow control when the data volume exceeds a threshold. Each processing stage of the pipeline communicates via an event bus, reducing coupling between modules. An exception handling mechanism monitors the execution status of each stage and automatically initiates a recovery process upon detecting an error.

[0070] The physical cache address set is maintained using copy-on-write technology to ensure data integrity during path strategy updates. The address mapping table uses a sparse data structure for storage, optimizing memory usage efficiency. Cache consistency is guaranteed through a version number mechanism, ensuring that read operations always access a complete version snapshot. Address resolution is accelerated using multi-level caching, reducing memory access latency. The system periodically defragments the address set to maintain memory space continuity.

[0071] The network transport layer is optimized for the characteristics of video data, using fragmented transmission for large data packets and aggregated transmission for small data packets. The transport protocol supports priority marking to ensure critical data packets are processed first. Congestion control algorithms combined with active queue management prevent performance degradation caused by network overload. The error recovery mechanism employs selective retransmission, retransmitting only the data that was actually lost. The transport layer connects to upper-layer applications through standard interfaces, facilitating the replacement and upgrade of different components.

[0072] This embodiment adopts a layered architecture, from the bottom-level physical resource management to the upper-level decision-making and scheduling, with each layer interacting through well-defined interfaces. System status monitoring runs through all layers, providing a comprehensive view of operational status. Configuration management supports dynamic parameter adjustment to adapt to different workloads and performance requirements. Special attention was paid to resource utilization efficiency during implementation to avoid unnecessary computational and storage overhead. The system design balances real-time and reliability requirements, ensuring low latency while guaranteeing accurate data processing.

[0073] Example 3: The pixel verifier employs an analysis method based on chromaticity plane projection in cross-node color space consistency detection. The detection process establishes a color deviation evaluation model to calculate the chromaticity difference between the current pixel and the reference standard.

[0074]

[0075] in Indicates the degree of chromaticity difference. and The chromaticity component represents the pixel being detected. and This represents the color space reference value. The calculation is performed in batches of 8×8 pixel blocks, and the results are stored in a dedicated difference buffer. The buffer employs a double-buffered design, allowing the detection algorithm to work in parallel with subsequent processing modules. When three consecutive blocks... When the value exceeds the threshold, the verifier generates a correction instruction package containing the coordinate range and the deviation.

[0076] The dynamic compensator implements nonlinear adjustment based on sensing characteristics. The processing is divided into two parallel pipelines: luminance compensation and chrominance compensation. Luminance compensation employs a piecewise linear transformation, dividing the input luminance range into 16 uniform sub-intervals, each applying an independent slope coefficient. The coefficient selection is based on... The mapping relationship between the values ​​and the preset response curve ensures that the adjustment range matches the perceptual sensitivity. Chromaticity compensation performs a vector rotation operation in UV space, with the rotation angle proportional to the chromaticity difference, and the maximum adjustment range limited to ±5 degrees. The compensated pixel data undergoes anti-banding filtering to eliminate any potential step effects.

[0077] The load monitoring unit employs an event-driven sampling strategy, with the core metric collection interval dynamically changing according to system load. Processor utilization is directly obtained through performance counters, measuring the proportion of actual instruction execution cycles to total cycles. Memory bandwidth utilization is sampled through memory controller registers, recording the data transfer volume for each refresh cycle. Network status monitoring uses a hybrid approach: hardware counters provide basic throughput data, while software probes supplement the measurement of queue depth and latency. All sampled data is appended with timestamps and node identifiers and transmitted to the central analysis module via a message queue.

[0078] The priority mapping unit implements a rule-based decision-making system. The rule base contains three types of evaluation strategies: real-time strategies focus on processing latency and deadlines, resource strategies evaluate memory and bandwidth utilization, and quality strategies consider color consistency and frame integrity. The rule matching process is optimized using the Rete algorithm to efficiently identify input performance indicator patterns. The decision output is a multi-dimensional weight vector containing adjustment suggestions for CPU priority, memory quota, and network bandwidth. The weight vector is normalized to ensure the comparability of values ​​in each dimension, and the final result is quantized into integer values ​​from 0 to 100.

[0079] The instruction generation unit converts weight vectors into machine-executable control commands. CPU control commands specify frequency scaling strategies and core allocation schemes, employing DVFS technology to achieve energy efficiency balance. Memory commands control allocation granularity and reclamation thresholds, optimizing TLB efficiency by adjusting page size. Network commands set traffic shaping parameters and QoS levels, using a hierarchical token bucket algorithm to ensure fair bandwidth allocation. Command encoding uses a compact binary format, containing three parts: opcode, target node, and parameter list. The command queue implements a priority-based queueing mechanism, allowing critical configuration changes to interrupt ongoing ordinary commands.

[0080] The resource reallocator employs hot page migration technology when performing physical memory adjustments. The migration process first reserves contiguous address space on the target node, then gradually copies active pages, and finally atomically switches address mappings. The memory compression algorithm performs transparent compression on inactive pages, using the LZ4 algorithm to balance compression ratio and speed. Network bandwidth reallocation is implemented through virtual channels, each independently maintaining credit counts and rate limits. A feasibility check is performed before any adjustment operation to ensure the system always retains necessary emergency resources.

[0081] The color management subsystem maintains a device-independent color processing pipeline. Input data is first converted to the XYZ color space, and then mapped to the target gamut based on the target profile. The mapping process preserves metadata information, supporting reverse conversion in subsequent processing stages. Reference values ​​are calibrated periodically, taking into account ambient lighting conditions and display device characteristics. The color matching algorithm uses an improved version of the CIE2000 color difference formula, reducing computational complexity while maintaining accuracy.

[0082] The exception handling framework establishes a multi-level recovery mechanism. The first level attempts automatic repair, such as retries failed operations or switching to an alternative path. The second level triggers a partial reset, reinitializing the affected functional modules. The third level performs a system-level rollback, restoring to the most recent valid checkpoint. The recovery process records detailed tracking information, including the exception type, handling steps, and final result. This tracking data is used to optimize exception detection thresholds and recovery strategies.

[0083] The real-time debugging interface provides access to low-level state. The diagnostic protocol supports remote reading of register contents and memory snapshots, employing differential encoding to reduce data transmission. Performance analysis tools can inject probe code to dynamically measure the execution time of specific functions. Debugging sessions are conducted through a dedicated channel to avoid interfering with normal business data flow. Access control mechanisms restrict execution permissions for sensitive operations, preventing unauthorized configuration modifications.

[0084] A persistent configuration system enables versioned parameter management. Each configuration change generates an incremental snapshot, recording the modifications and context information. Snapshot storage employs copy-on-write technology to ensure atomicity during version switching. Configuration rollback operations verify dependencies and automatically handle cascading updates of related parameters. Version history supports point-in-time queries and difference comparisons, aiding in the analysis of the impact of parameter adjustments on system behavior.

[0085] The visual monitoring interface enables multi-dimensional data correlation and display. Color-coded heatmaps intuitively present the load distribution of each node, while dynamic topology maps show changes in data flow paths. Trend charts support multi-timescale scaling, from millisecond-level real-time fluctuations to long-term operational trends. The alarm panel aggregates abnormal events from various modules and presents them categorized by severity. User interactions are logged, supporting operation sequence playback and audit trails.

[0086] Example 4: See Figure 4 The multi-layer blending unit receives standardized pixel matrices from different rendering nodes, and the processing is based on 8×8 pixel blocks. Each pixel block carries alpha channel information, and the blending operation uses an improved Porter-Duff compositing rule. The system maintains a Z-buffer depth buffer for the current frame, with depth values ​​represented by 16-bit fixed-point numbers, achieving a precision of 1 / 65536. During the blending operation, the input pixel is first compared with the depth value of the current buffer; the blending operation is only performed if the new pixel is in the foreground. The blended result is written to a temporary buffer, which is organized using a quadtree spatial index to accelerate subsequent access. A typical blending layer buffer data structure is shown in the table below:

[0087]

[0088] Temporal filter analysis is used to analyze motion characteristics between consecutive frames, employing a block-matching-based motion estimation method. The process divides the current frame into 16×16 macroblocks, searching for the optimal matching position within a 32-pixel radius of each macroblock. Motion vector calculation uses a three-step search method: first, an 8-pixel step size to determine the approximate direction; then, a 4-pixel step size to refine; and finally, a 1-pixel step size to determine the precise position. The filtering coefficients are dynamically adjusted based on motion intensity, with strong filtering applied to static areas and reduced filtering intensity in areas of rapid motion. The compensation algorithm considers the continuity of the motion trajectory, smoothing abrupt changes in the vector.

[0089] The load monitoring unit collects real-time operational metrics from each node via performance counters, with a sampling period configurable from 1ms to 10ms. The collected metrics include over twenty microarchitecture-level parameters such as instruction throughput, cache hit rate, and branch prediction error rate. Data acquisition employs a non-intrusive design, reading information through dedicated performance monitoring registers to avoid interfering with normal program execution. Raw sampled data is filtered using moving averages and then stored in a time-series database, which uses columnar storage to optimize analysis and query efficiency.

[0090] The priority mapping unit converts the collected metrics into node load scores, employing a multi-level weighted algorithm. The first level processes processor-related metrics, including execution unit utilization and instruction parallelism. The second level processes memory subsystem metrics, considering cache hit rate and memory bandwidth usage. The third level evaluates the I / O subsystem status, analyzing network throughput and latency. Each level's score is normalized to the range of 0-100, and the final score is the geometric mean of the three levels. Score updates use an incremental calculation method, recalculating only the metrics that have changed.

[0091] The instruction generation unit generates resource adjustment commands based on the scoring results. The command format uses TLV (Type-Length-Value) encoding. Resource adjustment commands contain three basic elements: target value, transition time, and effective conditions. For CPU resource allocation, the command specifies the number of cores, frequency, and scheduling policy; memory adjustment controls the allocation ratio and reclamation policy; network bandwidth management sets rate limits and QoS levels. The instruction verifier checks the rationality of the commands to prevent contradictory configurations from being applied simultaneously.

[0092] The resource reallocator employs a gradual change strategy when performing specific adjustment operations. CPU core allocation gradually migrates threads using processor affinity masks, with each migration not exceeding 10% of the total number of threads. Memory adjustment uses a lazy reclamation strategy, reclaiming idle memory only when new allocation requests cannot be satisfied. Network bandwidth control achieves smooth rate limiting through a token bucket algorithm, with the bucket capacity dynamically calculated based on link latency. All adjustment operations are logged, including timestamps, operation types, parameter values, and execution results.

[0093] The transparency calculation for multi-layer blending employs a channel-specific processing mode. The RGB channels are calculated independently for the blending result, while the transparency channel influences the blending weights. For semi-transparent areas, the system enables edge anti-aliasing, smoothing pixel boundaries through multi-sampling. The blending modes support multiple preset schemes, including common image processing modes such as normal blending, overlay, and color filtering. Mode switching is achieved through a lookup table, where the table index corresponds to the blending mode code, and the table entry stores the corresponding blending function pointer.

[0094] During motion estimation, the system maintains a historical record of the motion vector field. The current vector is evaluated for reliability by comparing it with historical data; low-reliability vectors are marked as requiring special handling. Vector smoothing employs an optimization algorithm based on Markov random fields, considering the motion continuity of adjacent blocks. Temporal compensation predicts the pixel position of the next frame based on the motion trajectory, and a weighted average of the predicted and actual positions is applied. The compensation strength is inversely proportional to the motion estimation error; the larger the error, the smaller the compensation weight.

[0095] The visualization of performance monitoring data employs multi-dimensional correlation analysis. The system correlates raw metric data with resource adjustment operations, generating causal analysis charts. The timeline view displays metric trends and uses color to highlight key operation time points. The topology view shows the load distribution of each node, with node icon size representing CPU load and color depth representing memory pressure. The correlation matrix helps identify the interrelationships between metrics, enabling the optimization of monitoring strategies.

[0096] The configuration management system uses a declarative configuration language to describe resource allocation strategies. The strategy file defines the relationships between metric thresholds, response actions, and constraints. A syntax parser converts the strategy file into an internal decision tree, with each node containing both condition judgments and action execution. A strategy validator checks for rule conflicts and circular dependencies to ensure the consistency of the strategy logic. Strategy application uses a difference comparison algorithm, updating only the changed configuration parts.

[0097] The anomaly detection system establishes a multi-level alarm mechanism. Level 1 alarms detect short-term metric fluctuations and trigger log recording; Level 2 alarms detect persistent anomalies and initiate automatic repair processes; Level 3 alarms handle severe faults and trigger system protection mode. Alarm rules support dynamic threshold adjustment based on machine learning to adapt to changes in system operating modes. An alarm suppression mechanism prevents repeated alarms for the same problem, with suppression time increasing with alarm level.

[0098] The quality control module monitors the visual quality of the synthesized output. Quality assessment considers both static and dynamic features. Static features include sharpness and noise levels; dynamic features assess inter-frame coherence and motion smoothness. The assessment results are fed back to the synthesis parameter adjustment module, forming a closed-loop optimization system. A quality log records the quality scores of keyframes for long-term trend analysis and problem diagnosis.

[0099] The video pipeline's state management employs a finite state machine model. State transition conditions include factors such as frame processing progress, resource availability, and time constraints. The state machine design supports nested sub-states, allowing complex processing stages to be decomposed into multiple sub-states. State change events trigger corresponding handlers, and the handler execution results affect subsequent state transitions. A state history record is used for fault recovery, allowing rollback to any known good state.

[0100] Example 5: The output compression channel receives the final composite frame from the video compositing pipeline and employs an adaptive coding strategy based on content characteristics. The encoding process first preprocesses the input frame, including color space conversion and noise suppression. The system analyzes the pixel value distribution characteristics within the frame, dividing the image into three categories: static background areas, regular motion areas, and complex change areas. Different quantization parameters are used for different area types; a finer quantization step size is used for static areas, while the quantization precision is appropriately relaxed for complex change areas. The encoder supports dynamically adjusting the macroblock partitioning method, using smaller coding units in texture-rich areas and merging flat areas into larger processing blocks. The bitrate control algorithm comprehensively considers three factors: buffer state, network conditions, and image complexity, adjusting the compression intensity in real time.

[0101] The buffer controller continuously monitors the data backlog in the network's transmit buffer and employs a multi-level watermark management strategy. The lowest watermark triggers normal transmission mode, the intermediate watermark activates traffic shaping, and the highest watermark forcibly initiates a frame reduction strategy. Watermark thresholds are dynamically calculated based on network round-trip time, with higher thresholds set for long-latency links. Frame reduction decisions consider frame type priority, prioritizing the retention of key frames and reference frames while discarding some predicted frames. The frame reduction ratio is adjusted gradually, initially reducing the frame rate by 10%, and then gradually increasing the reduction based on buffer pressure, with a maximum of 30%. Detailed logs are recorded for each frame reduction operation, including timestamps, current frame rate, target frame rate, and buffer status.

[0102] The motion vector analysis module runs during the pre-encoding stage, extracting inter-frame motion features. The analysis process performs block matching between the current frame and reference frames, calculating the motion vector and matching error for each 16×16 macroblock. Motion field smoothing eliminates outlier vectors, and median filtering and vector field interpolation improve the continuity of motion trajectories. A motion intensity grading algorithm divides the image into high, medium, and low motion level regions, with the grading results guiding subsequent bitrate allocation. Vector analysis data is also used for frame type decisions: extending keyframe intervals for static scenes and increasing reference frame density for dynamic scenes.

[0103] The dynamic rate encoder employs a hybrid coding architecture, combining intra-frame prediction and inter-frame prediction modes. Intra-frame prediction supports 33 directional prediction modes, selecting the optimal prediction direction through rate-distortion optimization. Inter-frame prediction utilizes motion vector analysis results, employing multi-reference frame prediction with an adaptive search range. During the transform coding stage, either DCT or DST transform is selected based on content characteristics, and the quantization matrix is ​​optimized for human visual characteristics. Entropy coding uses context-based adaptive binary arithmetic coding, with the probability model dynamically updated based on the encoded data. A real-time adjustment mechanism for coding parameters monitors the actual compression rate of the output bitstream, immediately correcting the quantization parameters when deviations from the target.

[0104] The network transmission buffer employs a circular queue structure, enabling lock-free concurrent access for producers and consumers. Buffer management records the enqueue time, priority flag, and dependencies for each data packet. The transmission scheduler determines the transmission order based on packet priority and network conditions, allowing critical packets to be transmitted in advance. Buffer status monitoring includes assessments of three dimensions: fill level, average dwell time, and overflow risk. When the buffer is detected to be nearing saturation, the controller sends a backpressure signal to the frame assembly module, requesting a reduction in the data production rate. The backpressure signal carries detailed status information of the current buffer, providing decision-making reference for upstream modules.

[0105] The frame rate reduction instruction generation logic comprehensively considers multiple factors, including buffer fill trends, network throughput changes, and receiver feedback. The instruction encoding uses a compact binary format, including the frame rate adjustment range, effective time, and exception conditions. Instruction transmission is completed through a high-priority control channel to ensure timely delivery to the processing module. An instruction verification mechanism checks the reasonableness of the frame rate reduction ratio to prevent excessively low settings from causing stuttering. The instruction execution result is fed back to the buffer controller via an acknowledgment message, forming a closed-loop control system.

[0106] The adaptive bitrate algorithm dynamically adjusts transmission parameters based on network probing results. The active probing module periodically sends test data packets to measure path bandwidth, latency, and packet loss rate. Passive monitoring analyzes the transmission latency and acknowledgment interval of actual data packets. The probing data, after being processed by a sliding window filter, is input into the bitrate calculation model. The model outputs a suggested transmission bitrate, which the encoder adjusts its compression parameters accordingly. Bitrate changes employ a smooth transition strategy to avoid unstable image quality caused by drastic fluctuations. The network congestion detection algorithm identifies bottleneck nodes on the transmission path and implements corresponding mitigation measures for different congestion types.

[0107] Video data packet encapsulation follows a layered header structure, with each layer carrying specific functional information. The transport layer header includes a sequence number, timestamp, and frame type marker; the application layer header records encoding parameters, reference frame relationships, and fragmentation information. The encapsulation process employs zero-copy technology to avoid multiple copies of data in memory. Large frames are automatically fragmented into data packets suitable for network transmission, and the receiving end reassembles the complete frame based on the fragmentation information. The encapsulation format supports extended fields to facilitate the addition of new features in the future.

[0108] Anomaly recovery mechanisms handle various anomalies in network transmission. Packet loss detection identifies missing data packets through sequence number comparison and triggers selective retransmission requests. The out-of-order reassembly buffer reorders received data packets to eliminate the impact of network jitter. Error concealment techniques utilize spatiotemporal correlations to recover lost pixel regions, including frame copying, motion compensation, and interpolation. Recovery strategies are intelligently selected based on packet loss location and frame type to maximize visual continuity. The recovery process records detailed diagnostic information for network quality assessment and coding parameter optimization.

[0109] The quality control closed-loop system continuously monitors objective quality indicators of the output video. The quality assessment module analyzes data across dimensions such as compression distortion, inter-frame jitter, and color fidelity. Assessment results are correlated with encoding parameters to establish a three-dimensional optimization model of quality, bitrate, and complexity. The model outputs parameter adjustment suggestions, which the encoder uses to fine-tune the quantization matrix and prediction mode selection. Long-term quality trend data is used for offline analysis to identify systematic quality fluctuation patterns. The quality log records the assessment results of keyframes, supporting querying and statistical analysis by time range.

[0110] The configuration management system maintains the version history of all adjustable parameters. Parameters are grouped into three main categories: coded parameters, network parameters, and buffer management parameters, each with its own independent update strategy. Coded parameters are updated incrementally, network parameters take effect immediately, while buffer parameters require restarting the relevant modules. Configuration changes are atomically guaranteed through a transaction mechanism, automatically rolling back to the previous state in case of failure. The parameter validator checks the rationality and compatibility of new values ​​to prevent invalid configurations from causing system anomalies. Configuration distribution uses an eventual consistency model, allowing for brief inconsistencies in the states of different modules.

[0111] The system monitoring interface displays key operational metrics in real time, including instantaneous bitrate, buffer utilization, and network packet loss rate. Visualization components support multi-dimensional data correlation analysis, such as trend comparisons between bitrate and quality. The alarm panel centrally displays abnormal events, presented in severity categories. Diagnostic tools can replay system status changes over specific time periods to aid in problem localization. The interface design prioritizes operational efficiency, with frequently used functions supporting keyboard shortcuts. Displayed content can be customized based on user roles, distinguishing between views for system administrators and regular operators.

[0112] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0113] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-latency video stream synthesis and output system for rendering nodes, characterized in that, include: The input proxy module is used to capture video stream data packets transmitted by multiple rendering nodes, parse the data packet header information in real time to generate metadata tags, and write the metadata tags into a shared memory queue. The frame synchronization manager dynamically connects to the shared memory queue, establishes a cross-node frame synchronization index table based on the timestamp sequence of the metadata tags, and triggers a frame filling instruction when consecutive timestamps are detected to be missing. The frame buffer allocator generates virtual frame placeholders in response to the frame interpolation instruction, and simultaneously allocates physical buffer addresses for video stream data packets of the complete timestamp sequence after frame interpolation, and generates a frame storage mapping table. The compositing decision engine periodically polls the frame storage mapping table, calculates the frame space weight based on the topological location of the rendering node, and generates a distributed compositing path strategy by combining the physical distribution of cache addresses. The synthetic decision engine includes a topology analysis unit and a weight calculation unit; The topology analysis unit parses the network coordinates and hardware configuration parameters of the rendering nodes to generate a three-dimensional spatial distribution model. The weight calculation unit calculates the spatial influence coefficient of each rendering node frame by frame based on the node spacing and bandwidth capacity in the three-dimensional spatial distribution model. The physical distribution refers to the memory distribution characteristics of the NUMA architecture within the server.

2. The low-latency video stream synthesis and output system for rendering nodes as described in claim 1, characterized in that, The frame synchronization manager also includes: The timing verification unit extracts metadata tags within a continuous time window from the shared memory queue and calculates the timestamp difference between adjacent tags to generate a timing discreteness matrix. When the variance of the temporal discreteness matrix exceeds a preset fluctuation threshold, the index building unit activates a backtracking mechanism to re-align the timestamp sequence and outputs the updated cross-node frame synchronization index table to the physical cache allocator.

3. The low-latency video stream synthesis and output system for rendering nodes as described in claim 2, characterized in that, The frame buffer allocator specifically performs the following: The virtual frame generator, upon receiving the frame interpolation instruction, creates a virtual frame carrying empty pixel data and marks the virtual frame identifier and associated rendering node number in the frame storage mapping table. The physical address allocator scans the cross-node frame synchronization index table to obtain the complete timestamp sequence, allocates exclusive memory blocks for each video stream data packet, and generates a set of physical cache addresses containing memory block pointers and timestamp binding relationships.

4. The low-latency video stream synthesis and output system for rendering nodes as described in claim 3, characterized in that, Also includes: The frame assembly controller reads video stream data packets in batches from the physical cache address set, reassembles pixel data blocks according to the instruction order of the distributed synthesis path strategy, and pushes the reassembled data packets to the video synthesis pipeline. The pipeline status monitor captures the processing delay parameters of the video compositing pipeline in real time and feeds back the delay parameters to the compositing decision engine to trigger path strategy updates.

5. The low-latency video stream synthesis and output system for rendering nodes as described in claim 4, characterized in that, The synthetic decision engine also includes: The path generation unit dynamically constructs a synthetic data stream path that minimizes the number of transmission hops by combining the spatial influence coefficient with the physical cache address distribution in the frame storage mapping table.

6. The low-latency video stream synthesis and output system for rendering nodes as described in claim 5, characterized in that, The frame assembly controller further includes: The pixel verifier performs cross-node color space consistency checks during the reassembly of pixel data blocks and generates color correction instructions when a color gamut offset is detected. The dynamic compensator adjusts the brightness mapping curve of the pixel data block in response to the color correction command and outputs a normalized pixel matrix to the video compositing pipeline.

7. The low-latency video stream synthesis and output system for rendering nodes as described in claim 6, characterized in that, Also includes: The priority scheduler receives the latency parameters from the pipeline status monitor and the frame interpolation instructions from the frame synchronization manager, and calculates the real-time load priority of the rendering nodes. The resource reallocator dynamically adjusts the generation frequency of virtual frame placeholders according to the real-time load priority and sends a memory block expansion request to the physical cache allocator.

8. The low-latency video stream synthesis and output system for rendering nodes as described in claim 7, characterized in that, The video compositing pipeline includes: The multi-layer blending unit receives the standardized pixel matrix and performs a transparency overlay operation to generate a blending layer cache. A temporal filter performs inter-frame motion vector analysis on the hybrid layer buffer, compensates for temporal continuity parameters based on the analysis results, and outputs the final synthesized frame.

9. The low-latency video stream synthesis and output system for rendering nodes as described in claim 8, characterized in that, The specific operations of the priority scheduler include: The load monitoring unit periodically collects the CPU utilization and network throughput of the rendering nodes; The priority mapping unit inputs the CPU utilization and network throughput into the multidimensional decision model and outputs the real-time scheduling weight coefficients of each rendering node. The instruction generation unit compares the real-time scheduling weight coefficient with the preset load threshold to generate a resource adjustment instruction set that includes the memory expansion ratio and the frame interpolation rate.

10. The low-latency video stream synthesis and output system for rendering nodes as described in claim 9, characterized in that, Also includes: The output compression channel receives the final synthesized frame and performs dynamic bitrate encoding based on motion vectors to write the compressed data stream into the network transmission buffer. The buffer controller monitors the filling status of the network transmission buffer and sends a frame downsizing command to the frame assembly controller when the filling amount exceeds the window threshold.

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