Adaptive Bitrate Streaming in Wireless Networks With Transcoding at Network Edge Using Deep Reinforcement Learning

Guo, Yashuang; Yu, F. Richard; An, Jianping; Yang, Kai; Yu, Chuqiao; Leung, Victor C. M.

doi:10.1109/tvt.2020.2968498

Cited by 51 publications

(19 citation statements)

References 45 publications

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“…They designed a deep learning approach for recognizing the interestingness of the video content and a DQN approach for rate adaptation according to incorporating video interestingness information. Considering joint computation and communication for ABR streaming, Guo et al [56] presented a joint video transcoding and quality adaptation framework for ABR streaming. Inspired by recent advances of blockchain technology, Liu et al [57] proposed a novel DRL-based transcoder selection framework for blockchain-enabled D2D transcoding systems where video transcoding has been widely adopted in live streaming services, to bridge the resolution and format gap between content producers and consumers.…”

Section: Drl-based Transcoding Schedulingmentioning

confidence: 99%

“…In another way, if more than one chunk is played before the next chunk arrives, then, the buffer is depleted and the rebuffering is happened. So, in the rebuffer model, the term of rebuffering time and buffered video time are usually introduced, which are used in Reference [56]. A video has some chunks; each chunk also contains a fixed duration of video, such as D seconds of video.…”

Section: Rebuffer Modelmentioning

confidence: 99%

“…Beside the bandwidth cost, the video streaming service also needs to consider the transcoding cost. Based on the definition and description of video transcoding in [56,62], the transcoding cost is closely related to the input bit-rate, target bit-rate, the video length, and the number of CPU cores needed for transcoding according to the video pricing model. Then, we define the transcoding cost incurred at time stage t as…”

Section: System Cost Modelmentioning

confidence: 99%

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Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

Wan

2020

Wireless Communications and Mobile Computing

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In the next-generation wireless communications system of Beyond 5G networks, video streaming services have held a surprising proportion of the whole network traffic. Furthermore, the user preference and demand towards a specific video might be different because of the heterogeneity of users’ processing capabilities and the variation of network condition. Thus, it is a complicated decision problem with high-dimensional state spaces to choose appropriate quality videos according to users’ actual network condition. To address this issue, in this paper, a Content Distribution Network and Cluster-based Mobile Edge Computing framework has been proposed to enhance the ability of caching and computing and promote the collaboration among edge severs. Then, we develop a novel deep reinforcement learning-based framework to automatically obtain the intracluster collaborative caching and transcoding decisions, which are executed based on video popularity, user requirement prediction, and abilities of edge servers. Simulation results demonstrate that the quality of video streaming service can be significantly improved by using the designed deep reinforcement learning-based algorithm with less backhaul consumption and processing costs.

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Section: Drl-based Transcoding Schedulingmentioning

confidence: 99%

Section: Rebuffer Modelmentioning

confidence: 99%

Section: System Cost Modelmentioning

confidence: 99%

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Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

Wan

2020

Wireless Communications and Mobile Computing

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“…In [16,17], algorithms are proposed to dynamically adjust the quality of experience for video streaming application in MEC. To utilize the computing resource of the MEC server, adaptive bitrate streaming approaches are presented in [18][19][20]. By the estimation of wireless channel and assistance of the MEC server, video quality is adapted to the wireless channel variations.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Video Transmission Mechanism over MEC‐Based Content‐Centric Networks

Han

Zhao

Bao

et al. 2021

Wireless Communications and Mobile Computing

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The rapid growth of video traffic poses serious challenges to the current Internet. Content-Centric Networking (CCN) as a promising candidate has been proposed to reengineer the Internet architecture. The in-network caching and named content communication model of CCN can enhance the video streaming applications and reduce the network workload. Due to the bandwidth-consuming characteristic of video streaming, the aggressive transmission of video data will cause a reduction of overall network efficiency. In this paper, we present an adaptive video transmission mechanism over Mobile Edge Computing- (MEC-) based CCN. The computation and storage resources of the MEC server are utilized to facilitate the video delivery. Our mechanism adopts a scalable video coding scheme to adaptively control transmission rate to cope with the network condition variation. To analyse the equilibrium property of the proposed mechanism, an analytical model is deduced by using network utility function and convex programming. We also take into account the packet loss in wired and wireless links and present a MEC assistant loss recovery algorithm. The experiment results demonstrate the performance improvement of our proposed mechanism.

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“…Depending on the client network characteristics (e.g. bandwidth, latency), the rate adaptation algorithm of a media player requests segments with an appropriate bitrate [6,7] and aims to maintain a high quality of experience [8,9] by switching between segments with different qualities. Adaptive 360°v ideo streaming applications [10,11] widely adjust the video quality for the field of view and the rest of the image.…”

Section: Introductionmentioning

confidence: 99%

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

Agrawal

Zabrovskiy

Ilangovan³

et al. 2020

Cluster Comput

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HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.

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Adaptive Bitrate Streaming in Wireless Networks With Transcoding at Network Edge Using Deep Reinforcement Learning

Cited by 51 publications

References 45 publications

Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

An Adaptive Video Transmission Mechanism over MEC‐Based Content‐Centric Networks

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

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