# US 2026/0136050 A1 · Video transcoding task scheduling > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0136050 A1 - Document ID: US20260136050A1 - Status: Published application - Kind: A1 - Application number: 18/990,124 - Filed: 2024-12-20 - Published: 2026-05-14 - Earliest claimed: 2024-08-06 - Assignees: Beijing Baidu Netcom Science Technology Co., Ltd. - Applicants: Beijing Baidu Netcom Science Technology Co., Ltd. - Inventors: Huaifei XING - Web page: https://patentgrep.com/patent/US20260136050A1 ## CPC - H04N 21/234309: {by transcoding between formats or standards, e.g. from MPEG-2 to MPEG-4 or from Quicktime to Realvideo (conversion of standards in analog television systems H04N7/01)} - H04N 21/2405: {Monitoring of the internal components or processes of the server, e.g. server load (allocation of resources in multiprogramming arrangements G06F9/50; performance measurement of computer activity G06F11/34)} - H04N 21/23103: {using load balancing strategies, e.g. by placing or distributing content on different disks, different memories or different servers (storage management G06F3/0604; allocation of resources considering the load in multiprogramming arrangements G06F9/505; techniques for rebalancing the load in a distributed system G06F9/5083; access to distributed or replicated servers, e.g. load balancing, in data networks H04L67/1001)} - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A method is provided for scheduling a video transcoding task, and relates to the field of data processing technology, and in particular to artificial intelligence and streaming media technology. The implementation is: determining an input video to be transcoded, wherein the input video includes at least one image frame; performing an image analysis on the at least one image frame to obtain an image feature of the input video; determining target a transcoding parameter for a transcoded target video; predicting, based on the image feature of the input video and the target transcoding parameter, a resource occupancy required for a transcoding task for the input video and an acceptable memory usage for the transcoding task; scheduling a computational resource for the transcoding task based on the predicted resource occupancy and memory usage. ## Claims ### Claim 1 A method for scheduling a video transcoding task, comprising: determining an input video to be transcoded, wherein the input video comprises at least one image frame; performing an image analysis on the at least one image frame to obtain an image feature of the input video; determining a target transcoding parameter for a transcoded target video; predicting, based on the image feature of the input video and the target transcoding parameter, a resource occupancy required for a transcoding task of the input video and an acceptable memory usage for the transcoding task; scheduling, based on the predicted resource occupancy and memory usage, a computational resource for the transcoding task. ### Claim 2 (depends on 1) The method of claim 1, wherein the image feature of the input video comprises at least one of: a resolution; a bit rate; a group of pictures size; a frame rate; an encoder type; temporal complexity information; spatial complexity information. ### Claim 3 (depends on 1) The method of claim 1, wherein the target transcoding parameter comprises at least one of: a target resolution, a target bit rate, a target frame rate, and a target encoding level. ### Claim 4 (depends on 1) The method of claim 1, wherein scheduling, based on the predicted resource occupancy and memory usage, the computational resource for the transcoding task comprises: obtaining a current processor utilization rate and a current memory usage of each server of a server cluster which performs the video transcoding task; determining, by comparing the predicted resource occupancy with the current processor utilization rate and the predicted memory usage and the current memory usage respectively, a server which meets a predetermined scheduling condition from the server cluster for processing the transcoding task. ### Claim 5 (depends on 1) The method of claim 1, wherein predicting, based on the image feature of the input video and the target transcoding parameter, the resource occupancy and the memory usage comprises: processing the image feature of the input video and the target transcoding parameter using a trained machine learning model to output the resource occupancy and the memory usage, wherein the input video to be transcoded is a live stream video, and the machine learning model is a regression model or a classification model. ### Claim 6 (depends on 5) The method of claim 5, wherein the machine learning model is trained by: determining a sample dataset, wherein the sample dataset comprises a plurality of sample videos to be transcoded; for each sample video in the sample dataset, performing an image analysis on an image frame in the sample video to determine a sample image feature of the sample video; determining a target transcoding parameter corresponding to the sample video; transcoding the sample video according to the target transcoding parameter; marking a ground truth resource occupancy required for a transcoding process of the sample video and an acceptable ground truth memory usage for the transcoding process; inputting the sample image feature into the machine learning model to obtain a predicted resource occupancy and a predicted memory usage; adjusting a parameter of the machine learning model based on a first difference between the predicted resource occupancy and the ground truth resource occupancy and a second difference between the predicted memory usage and the ground truth memory usage. ### Claim 7 (depends on 6) The method of claim 6, wherein marking the ground truth resource occupancy required for the transcoding process of the sample video and the acceptable ground truth memory usage for the transcoding process comprises: increasing a number of a plurality of transcoding tasks being executed in parallel to transcode the sample video based on the target transcoding parameter, until at least one of the plurality of transcoding tasks stutters; marking a resource occupancy and a memory usage of the plurality of transcoding tasks when the transcoding task of the sample video stutters; determining the ground truth resource occupancy of each transcoding task based on the resource occupancy of the plurality of transcoding tasks and the number of the plurality of transcoding tasks; determining the ground truth memory usage based on a server memory usage when the transcoding task stutters. ### Claim at position 8 -14. (canceled). ### Claim 15 An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform operations of: determining an input video to be transcoded, wherein the input video comprises at least one image frame; performing an image analysis on the at least one image frame to obtain an image feature of the input video; determining a target transcoding parameter for a transcoded target video; predicting, based on the image feature of the input video and the target transcoding parameter, a resource occupancy required for a transcoding task of the input video and an acceptable memory usage for the transcoding task; scheduling, based on the predicted resource occupancy and memory usage, a computational resource for the transcoding task. ### Claim 16 A non-transient computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to perform the operations of: determining an input video to be transcoded, wherein the input video comprises at least one image frame; performing an image analysis on the at least one image frame to obtain an image feature of the input video; determining a target transcoding parameter for a transcoded target video; predicting, based on the image feature of the input video and the target transcoding parameter, a resource occupancy required for a transcoding task of the input video and an acceptable memory usage for the transcoding task; scheduling, based on the predicted resource occupancy and memory usage, a computational resource for the transcoding task. ### Claim 17 (canceled) ### Claim 18 (depends on 15) The electronic device of claim 15, wherein the image feature of the input video comprises at least one of: a resolution; a bit rate; a group of pictures size; a frame rate; an encoder type; temporal complexity information; spatial complexity information. ### Claim 19 (depends on 15) The electronic device of claim 15, wherein the target transcoding parameter comprises at least one of: a target resolution, a target bit rate, a target frame rate, and a target encoding level. ### Claim 20 (depends on 15) The electronic device of claim 15, wherein scheduling, based on the predicted resource occupancy and memory usage, the computational resource for the transcoding task comprises: obtaining a current processor utilization rate and a current memory usage of each server of a server cluster which performs the video transcoding task; determining, by comparing the predicted resource occupancy with the current processor utilization rate and the predicted memory usage and the current memory usage respectively, a server which meets a predetermined scheduling condition from the server cluster for processing the transcoding task. ### Claim 21 (depends on 15) The electronic device of claim 15, wherein predicting, based on the image feature of the input video and the target transcoding parameter, the resource occupancy and the memory usage comprises: processing the image feature of the input video and the target transcoding parameter using a trained machine learning model to output the resource occupancy and the memory usage, wherein the input video to be transcoded is a live stream video, and the machine learning model is a regression model or a classification model. ### Claim 22 (depends on 21) The electronic device of claim 21, wherein the machine learning model is trained by: determining a sample dataset, wherein the sample dataset comprises a plurality of sample videos to be transcoded; for each sample video in the sample dataset, performing an image analysis on an image frame in the sample video to determine a sample image feature of the sample video; determining a target transcoding parameter corresponding to the sample video; transcoding the sample video according to the target transcoding parameter; marking a ground truth resource occupancy required for a transcoding process of the sample video and an acceptable ground truth memory usage for the transcoding process; inputting the sample image feature into the machine learning model to obtain a predicted resource occupancy and a predicted memory usage; adjusting a parameter of the machine learning model based on a first difference between the predicted resource occupancy and the ground truth resource occupancy and a second difference between the predicted memory usage and the ground truth memory usage. ### Claim 23 (depends on 22) The electronic device of claim 22, wherein marking the ground truth resource occupancy required for the transcoding process of the sample video and the acceptable ground truth memory usage for the transcoding process comprises: increasing a number of a plurality of transcoding tasks being executed in parallel to transcode the sample video based on the target transcoding parameter, until at least one of the plurality of transcoding tasks stutters; marking a resource occupancy and a memory usage of the plurality of transcoding tasks when the transcoding task of the sample video stutters; determining the ground truth resource occupancy of each transcoding task based on the resource occupancy of the plurality of transcoding tasks and the number of the plurality of transcoding tasks; determining the ground truth memory usage based on a server memory usage when the transcoding task stutters. ### Claim 24 (depends on 15) The non-transient computer-readable storage medium of claim 15, wherein the image feature of the input video comprises at least one of: a resolution; a bit rate; a group of pictures size; a frame rate; an encoder type; temporal complexity information; spatial complexity information. ### Claim 25 (depends on 15) The non-transient computer-readable storage medium of claim 15, wherein the target transcoding parameter comprises at least one of: a target resolution, a target bit rate, a target frame rate, and a target encoding level. ### Claim 26 (depends on 15) The non-transient computer-readable storage medium of claim 15, wherein scheduling, based on the predicted resource occupancy and memory usage, the computational resource for the transcoding task comprises: obtaining a current processor utilization rate and a current memory usage of each server of a server cluster which performs the video transcoding task; determining, by comparing the predicted resource occupancy with the current processor utilization rate and the predicted memory usage and the current memory usage respectively, a server which meets a predetermined scheduling condition from the server cluster for processing the transcoding task. ### Claim 27 (depends on 15) The non-transient computer-readable storage medium of claim 15, wherein predicting, based on the image feature of the input video and the target transcoding parameter, the resource occupancy and the memory usage comprises: processing the image feature of the input video and the target transcoding parameter using a trained machine learning model to output the resource occupancy and the memory usage, wherein the input video to be transcoded is a live stream video, and the machine learning model is a regression model or a classification model. ### Claim 28 (depends on 21) The non-transient computer-readable storage medium of claim 21, wherein the machine learning model is trained by: determining a sample dataset, wherein the sample dataset comprises a plurality of sample videos to be transcoded; for each sample video in the sample dataset, performing an image analysis on an image frame in the sample video to determine a sample image feature of the sample video; determining a target transcoding parameter corresponding to the sample video; transcoding the sample video according to the target transcoding parameter; marking a ground truth resource occupancy required for a transcoding process of the sample video and an acceptable ground truth memory usage for the transcoding process; inputting the sample image feature into the machine learning model to obtain a predicted resource occupancy and a predicted memory usage; adjusting a parameter of the machine learning model based on a first difference between the predicted resource occupancy and the ground truth resource occupancy and a second difference between the predicted memory usage and the ground truth memory usage.