Hls processing method based on multi-cloud storage

Abstract

The invention relates to the technical field of cloud storage, in particular to an hls processing method based on multi-cloud storage, which comprises the following steps: S100, determining at least two cloud data ends for data storage, and ensuring that the two cloud data ends can serve an output terminal at the same time; s200, determining the storage state of the dynamic cloud data end, giving an available storage space corresponding to the cloud data end according to the total storage data amount of the cloud data end corresponding to the node moment, and determining a corresponding available coefficient; and S300, according to the corresponding available coefficients, determining slice sizes corresponding to different cloud data ends according to a slicing method, and allocating the slice sizes to corresponding playlists. By introducing the concept of available coefficients into hls, on one hand, the sizes of slices transmitted in real time are adjusted, and on the other hand, adaptive adjustment can be synchronously carried out on the number of transmitted slices; particularly, through a set grouping method, the video playing stability of the video playing end can be effectively ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud storage, in particular to a hls processing method based on multi-cloud storage. BACKGROUND

[0002] Cloud storage is an online storage mode, that is, data is stored on virtual servers usually managed by a third party instead of a dedicated server; a data center operator prepares storage virtualization resources in the back end according to customer needs and provides them in the form of a storage resource pool, and customers can use this storage resource pool to store files or objects; HLS is a live streaming protocol that provides real-time video + audio experience for the public using the widely used HTTP technology.

[0003] The small slice method of the existing HLS protocol generates a large number of files, and storing or processing these files will cause a large waste of resources, significantly increase the burden on the CPU, and may cause the device to run unstable, affecting its normal function, and in view of this, we propose a hls processing method based on multi-cloud storage. SUMMARY

[0004] In view of the above shortcomings of the prior art, the present application provides a hls processing method based on multi-cloud storage, which can effectively solve the problem that the small slice method of the existing HLS protocol generates a large number of files, and storing or processing these files will cause a large waste of resources, significantly increase the burden on the CPU, and may cause the device to run unstable, affecting its normal function.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme:

[0006] The present application provides a hls processing method based on multi-cloud storage, comprising the following steps:

[0007] S100: Determine at least two cloud data terminals for data storage, and ensure that the two cloud data terminals can simultaneously serve one output terminal;

[0008] S200: Determine the storage state of the dynamic cloud data terminal, give the available storage space of the corresponding cloud data terminal according to the total amount of data stored in the cloud data terminal at the node time, and determine the corresponding available coefficient;

[0009] S300: According to the corresponding available coefficient, determine the slice size of the corresponding different cloud data terminal according to the slice method, and allocate to the corresponding playlist.

[0010] Further, the step S200 further comprises a grouping method for classifying different video content data sources, comprising the following steps: S210: dividing the data in the cloud data end into complete data and discrete data, wherein the complete data refers to data corresponding to the data group stored in the cloud data end, which can completely display a part of the video content, and the discrete data refers to part of the data corresponding to the data group stored in the cloud data end, which cannot support the playback of a part of the video content; S220: counting the specific video content corresponding to the complete data, forming a recommendable sequence, and adjusting the recommendable sequence in the recommendable sequence according to the video content popularity to form a primary adjustment sequence; S230: obtaining the primary adjustment sequence in different cloud data ends, dynamically adjusting the sequence content of the primary adjustment sequence in a sequential comparison manner to form a secondary adjustment sequence and providing the secondary adjustment sequence to the output terminal for selection.

[0011] Further, the step S200 further comprises a determination method of available coefficient, comprising the following steps: S240: determining the storage space of each cloud data end and the available margin, respectively; S250: configuring the available coefficient of the corresponding cloud data end according to the proportion of the available margin in the entire cloud data end, and slowing down or increasing the data receiving amount of the cloud data end according to the size of the available coefficient.

[0012] Further, the slicing method in the step S300 comprises the following steps: S310: according to the available coefficient of the corresponding cloud data end, the corresponding video content in different secondary adjustment sequences in the corresponding cloud data end is divided into slices with the same size as the available coefficient proportion; S320: adjusting the size of the available coefficient in a fixed time period re-coverage manner, and adjusting the size of the slice transmitted in the current time in each cloud data end.

[0013] Further, the step S400 further comprises adjusting the number of slices in each cloud data end, specifically comprising the following steps: S410: adjusting the number of slice transmissions per unit time according to the network bandwidth performance in the current video transmission link; S420: and dynamically adjusting the bit rate of the slice corresponding data content according to the number of slice transmissions per unit time.

[0014] Further, the step S420 further comprises a step S430: according to the number of slices received by the output terminal per unit time, if the number of slices exceeds a preset value, the slice transmission rate of at least one cloud data terminal is adjusted to ensure the smoothness of audio playback.

[0015] The technical scheme provided by the application has the following beneficial effects compared with the known prior art:

[0016] The application adjusts the slice size of real-time transmission by introducing the concept of available coefficient in hls, and can also adaptively adjust the number of transmission slices, and particularly, the grouping method can effectively ensure the stability of video playing of the video playing end. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0018] Figure 1 The figure is a schematic diagram of the overall process of the hls processing method of the present application.

[0019] Figure 2 The figure is a schematic diagram of the grouping method of the present application.

[0020] Figure 3 The figure is a schematic diagram of the slice number adjustment method of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0022] The following will further describe the present application in combination with the embodiments.

[0023] Embodiment: An hls processing method based on multi-cloud storage, comprising the following steps:

[0024] S100: Determine at least two cloud data ends for data storage, and ensure that the above two cloud data ends can simultaneously serve one output terminal;

[0025] S200: Determine the storage state of the dynamic cloud data end, give the available storage space of the corresponding cloud data end according to the total amount of data stored in the cloud data end at the node time, and determine the corresponding available coefficient;

[0026] S300: According to the corresponding available coefficient, determine the slice size of the corresponding different cloud data end according to the slice method, and allocate to the corresponding playlist.

[0027] In particular, with respect to step S300, as an embodiment, the slicing method includes that the original audio and video content is divided into a series of small, independent media segments, usually each segment lasts for a few seconds. These segments are usually stored in the format of H.264 video encoding and AAC audio encoding, but other encoding formats can also be used.

[0028] The step S200 also includes a grouping method for classifying different video content data sources, including the following steps:

[0029] S210: divide the data in the cloud data terminal into complete data and discrete data, wherein the complete data refers to the data corresponding to the data group stored in the cloud data terminal, which can completely display a part of the video content, and the discrete data refers to the part of the data corresponding to the data group stored in the cloud data terminal, which cannot support the playback of a part of the video content;

[0030] S220: count the specific video content corresponding to the complete data, form a recommendable sequence, and adjust the recommendable sequence according to the video content popularity in the recommendable sequence to form a primary adjustment sequence;

[0031] S230: obtain the primary adjustment sequence located in different cloud data terminals, dynamically regulate the sequence content of the primary adjustment sequence in a sequential comparison manner to form a secondary adjustment sequence and provide it to the output terminal for selection.

[0032] The step S200 also includes a determination method of available coefficient, including the following steps:

[0033] S240: determine the storage space of each cloud data terminal and the available margin, respectively;

[0034] S250: according to the proportion of the available margin in the entire cloud data terminal, configure the available coefficient of the corresponding cloud data terminal, and according to the size of the available coefficient, slow down or increase the data receiving amount of the cloud data terminal.

[0035] The slicing method in step S300 includes the following steps:

[0036] S310: according to the available coefficient of the corresponding cloud data terminal, divide the corresponding video content in the different secondary adjustment sequences in the corresponding cloud data terminal into slices with the same size as the proportion of the available coefficient;

[0037] S320: adjust the size of the available coefficient in a fixed time period re-coverage manner, and correspondingly adjust the slice size transmitted in each cloud data terminal at the current time.

[0038] It also includes step S400, which adjusts the number of slices in each cloud data terminal, specifically including the following steps:

[0039] S410: adjusting the number of slices transmitted per unit of time according to the network bandwidth performance in the current video transmission link;

[0040] S420: and dynamically adjusting the bit rate of the data content of the slices according to the number of slices transmitted per unit of time.

[0041] Step S420 is followed by step S430: if the number of slices received by the output terminal per unit of time exceeds a preset value, the slice transmission rate of at least one cloud data terminal is adjusted to ensure the smoothness of audio playback. Specifically, in the output terminal, the so-called output terminal refers to a player or the like with a playback function. The size of the media segment is not defined. As an implementation, the size of the slice is converted to a time of about 9 seconds or a part of a video played in the output terminal. According to the prior art, the output terminal receives at least three slices to start playing for the same video file. The time of playing the slice can provide the output terminal with the time of receiving the slice again, which can be understood as the buffer time. For different video resolutions, we can change the number of received slices and the playing time at any time as needed.

[0042] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements will not change the essence of the corresponding technical solutions out of the protection scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for hls processing based on multi-cloud storage, characterized in that, The method comprises the following steps: S100: determining at least two cloud data terminals for data storage, and ensuring that the two cloud data terminals can simultaneously serve one output terminal; S200: determining the storage state of the dynamic cloud data terminal, giving the available storage space of the corresponding cloud data terminal according to the total amount of data stored in the cloud data terminal at the node time, and determining the corresponding available coefficient; S300: according to the corresponding available coefficient, determining the slice size of the corresponding different cloud data terminal according to the slicing method, and allocating to the corresponding playlist. 2.The multi-cloud storage based hls processing method of claim 1, wherein, The grouping method in step S200 is used to classify and process different video content data sources, which comprises the following steps: S210: dividing the data in the cloud data terminal into complete data and discrete data, wherein the complete data refers to the data group stored in the cloud data terminal which can completely display a part of the video content, and the discrete data refers to the part of the data stored in the cloud data terminal which cannot support the playback of a part of the video content; S220: counting the specific video content corresponding to the complete data, forming a recommended sequence, and adjusting the recommended sequence according to the video content popularity in the recommended sequence to form a primary adjustment sequence; S230: obtaining the primary adjustment sequence located in different cloud data terminals, dynamically regulating the sequence content of the primary adjustment sequence in the form of sequential comparison to form a secondary adjustment sequence and provide it to the output terminal for selection. 3.The multi-cloud storage based hls processing method of claim 2, wherein, The determination method of the available coefficient in step S200 comprises the following steps: S240: determining the storage space and the available amount of each cloud data terminal; S250: configuring the available coefficient of the corresponding cloud data terminal according to the proportion of the available amount to the entire cloud data terminal, and slowing down or increasing the data receiving amount of the cloud data terminal according to the size of the available coefficient.

4. The method of claim 3, wherein, The slicing method in step S300 comprises the following steps: S310: according to the available coefficient of the corresponding cloud data terminal, dividing the corresponding video content in the different secondary adjustment sequences in the corresponding cloud data terminal into slices with the same size as the available coefficient proportion; S320: adjusting the size of the available coefficient in the form of re-covering in a fixed time period, and adjusting the slice size transmitted at the current time in each cloud data terminal.

5. The method of claim 1, wherein, Step S400 further comprises adjusting the number of slices in each cloud data terminal, specifically comprising the following steps: S410: adjusting the number of slice transmissions per unit time according to the network bandwidth performance in the current video transmission link; S420: and dynamically adjusting the bit rate of the slice corresponding data content according to the number of slice transmissions per unit time. 6.The multi-cloud storage based hls processing method of claim 1, wherein, Step S420 further comprises step S430: according to the number of slices received by the output terminal per unit time, if the number of slices exceeds the preset value, adjusting the slice transmission rate of at least one cloud data terminal to ensure the smoothness of audio playback.

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