Live video cloud transcoding resource allocation and scheduling method based on deep reinforcement learning

A technology for reinforcement learning and live video, applied in neural learning methods, selective content distribution, program control design, etc., can solve the problems affecting the operation of live broadcast services, transcoding timeout, changes in the number and length of live streams, etc.
CN110351571AActive Publication Date: 2019-10-18TSINGHUA UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TSINGHUA UNIV
Publication Date
2019-10-18

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Abstract

The invention provides a live video cloud transcoding resource allocation and scheduling method based on deep reinforcement learning, and belongs to the field of machine learning and the field of multimedia content processing. According to the method, in a training stage, a transcoding process is simulated in a simulation environment, a neural network capable of dynamically adjusting cloud computing resource allocation according to the workload change of a transcoding task is trained, and the transcoding task of live video content is scheduled on the allocated cloud computing resource; and inthe execution stage, a real-time decision of resource allocation is made according to the system state by using the trained neural network, and a transcoding task is scheduled on a cloud platform. Based on the deep reinforcement learning method, the cloud computing resource scale can be dynamically adjusted according to the transcoding task load change, and the resource use overhead is saved; based on the estimation method for the upper limit and the lower limit of the transcoding task time, scheduling execution of the transcoding task can be completed in time, and the service quality requirement is met.
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Description

technical field

[0001] The invention proposes a live video cloud transcoding resource allocation and scheduling method based on deep reinforcement learning, which belongs to the field of machine learning and multimedia content processing. Background technique

[0002] In recent years, mobile live broadcast platforms represented by Twitch, Douyu, and Kuaishou have achieved great success in the market. Currently, more than 3.2 million streamers start broadcasting on Twitch each month, with more than 150,000 daily active viewers. On these platforms, the production and production of video content is no longer limited to professional content production organizations, and a large number of ordinary users can also use various terminal devices to share life content such as games and entertainment with global audiences.

[0003] As a bridge connecting content producers and content consumers, the live broadcast platform needs to convert the video uploaded by the anchor into multiple ...

Claims

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