The growth of the video streaming business has led in an increase in demand for transcoding services from a wide range of consumers. Recent advancements in blockchain technology enable certain businesses to implement decentralized collaborative transcoding using device-to-device (D2D) networks, which pick a set of transcoders to execute transcoding collaboratively. In order to offer efficient and trustworthy transcoding services for block chain-enabled D2D transcoding systems, it is critical to collaboratively develop transcoder selection, job scheduling, and resource allocation algorithms.
In this series, I want to introduce how AI-powered agents can burst the process of video transcoding task. Firstly, we need to investigate the Blockchain-enabled transcoding systems to understand the importance of transcoding in practice.
Cloud-based video transcoding for dealing with computation intensive and time-consuming
With the exponential rise of mobile devices, video streaming services and applications have grown immensely in popularity and consumer interest. Online videos are exploding in popularity, accounting approximately 80 percent of all consumer Internet traffic by 2019 . To support a wide range of user devices (smartphones, laptop and desktop computers, TVs, and so on), the source video streams must be converted into multiple representations (bitrates, formats, resolutions, codecs, and so on) with various quality of service (QoS) types, which is extremely computationally intensive and time-consuming . To address this issue, cloud-based video transcoding is seen as a viable solution and has been extensively used by well-known firms such as Netflix, Amazon Prime, Vimeo, and Youtube, among others. –.
Despite their ability to deliver high-quality transcoding services, today's cloud-based transcoding platforms face certain challenges: 1) A long round-trip delay owing to the remote cloud server. 2) A significant strain on the backhaul lines while uploading and retrieving video material. 3) Transcoding services provide a low level of transparency and security.
Blockchain-Enabled video transcoding
To address the drawbacks of centralized cloud-based transcoding systems, a number of firms (such as Transcodium , for example) are leveraging developing blockchain technology to enable “crowdsourcing” transcoding with flexible monetization methods. Meanwhile, with end-device storage and processing capability increasing, blockchain may be deployed via device-to-device (D2D) networks, which provide a decentralized platform [7–11].
In comparison to existing cloud-based transcoding platforms, these upcoming blockchain-enabled ones can deliver more efficient transcoding services by dividing big video files into little bits and allocating them to a group of transcoders without the involvement of any intermediaries. Furthermore, content providers establish direct partnerships with transcoders via D2D networks, which can alleviate the high strain of backhaul cables. Furthermore, the encrypted smart contract can improve the security and transparency of transcoding jobs (e.g., file size, file length, task payment) , .
Current remaining problems of Blockchain-Enabled video transcoding
Nonetheless, blockchain-enabled transcoding over D2D networks is still in its early stages. Although it can stimulate distributed transcoder collaboration through incentive mechanisms, the transcoding efficiency and trustworthiness of blockchain-enabled collaborative transcoding have not been well addressed. There is, for example, a lack of an integrated assessment process for transcoder selection. Most existing blockchain-enabled services select transcoders at random or solely based on stakes (e.g., Theta , Livepeer ). However, the computing and communication capabilities of each transcoder have a significant influence on transcoding efficiency and should be taken into account as well. Meanwhile, to increase transcoding trustworthiness, the reputation method should be used for transcoder selection. Furthermore, transcoding job scheduling and the allocation of communication and computing resources have an impact on transcoding efficiency. As a result, how to jointly construct the transcoding job scheduling and resource allocation system is equally essential.
As a result, it is critical to provide an efficient system for addressing the concerns of transcoder selection, work scheduling, and resource allocation. Nonetheless, the dynamic and complicated properties of transcoding systems (e.g., workloads, QoS requirements, and candidate attributes) make standard optimization approaches challenging to use.
Next section introduction
In the next section, we will investigate how to deal with the Blockchain-Enabled video transcoding using deep reinforcement learning, an AI-powered optimization method.
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