Intelligent Video Bit Rate Adjustment and Bandwidth Allocation Method Based on Deep Learning

Abstract

The invention discloses an intelligent video bit rate adjustment and bandwidth allocation method based on deep learning, constructing a bit rate allocation neural network, whose input includes the normalized value of available bandwidth, the normalized value of the user's cached video duration, and the user's active state The state vector of , whose output is the code rate allocation scheme vector of each user; set several user states and available bandwidth scenarios to obtain training samples, and train the code rate allocation neural network; when each decision-making time slot arrives, obtain the state The vector is input to the trained code rate allocation neural network to obtain the code rate allocation scheme, and then the bandwidth occupied by each user is determined according to the code rate allocation scheme and the current available bandwidth to complete the bandwidth allocation. The invention utilizes the characteristics of fast execution speed of the inference process of the neural network, quickly calculates the decoding rate and bandwidth allocation scheme, and improves the timeliness and accuracy.

Description

technical field [0001] The invention belongs to the field of communication technology, and more specifically, relates to an intelligent video code rate adjustment and bandwidth allocation method based on deep learning. Background technique [0002] In recent years, the volume of video streaming business has increased dramatically and has occupied a major part of the entire Internet business. In order to adapt to dynamic network conditions and provide better service quality, most video service providers have deployed an HTTP-based Dynamic Adaptive Streaming (Dynamic Adaptive Streaming over HTTP, DASH) solution. In the DASH scheme, a video file is divided into segments of fixed segment length (in seconds). Each segment of the video can be encoded as a different copy using a different bit rate, and each bit rate corresponds to a specific video frame rate and resolution. Clients can adaptively request bitrates (different frame rates and resolutions) for the next segment based ...

AI Technical Summary

Problems solved by technology

When multiple DASH streams compete for the same bottleneck link, the existing adaptive bit rate adjustment algorithm has the following disadvantages: 1) Each client independently makes a bit rate adjustment algorithm without knowing the existence of other clients. Adaptive decision-making, lack of collaboration between clients; 2) Clients can only detect physical bandwidth at the application layer, lacking accurate network bandwidth information

[0014] However, it has been proven that this problem is NP-hard, and the solution cannot be guaranteed to be completed in polynomial time, and it cannot meet the delay-sensitive user business needs at this stage.

Images