A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

Bentaleb, Abdelhak; Taani, Bayan; Begen, Ali C.; Timmerer, Christian; Zimmermann, Roger

doi:10.1109/comst.2018.2862938

Cited by 349 publications

(179 citation statements)

References 155 publications

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“…Then the video player, placed on the client side, dynamically picks the proper bitrate for the next chunk to varying network conditions. Specifically, the bitrate decisions should achieve high bitrate and low rebuffering on the entire session [5,10]. We call it adaptive bitrate streaming (ABR).…”

Section: Background and Challenges 21 Video Transmission Servicesmentioning

confidence: 99%

Self-play reinforcement learning for video transmission

Huang¹,

Zhang²,

Sun³

2020

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video

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Video transmission services adopt adaptive algorithms to ensure users' demands. Existing techniques are often optimized and evaluated by a function that linearly combines several weighted metrics. Nevertheless, we observe that the given function fails to describe the requirement accurately. Thus, such proposed methods might eventually violate the original needs. To eliminate this concern, we propose Zwei, a self-play reinforcement learning algorithm for video transmission tasks. Zwei aims to update the policy by straightforwardly utilizing the actual requirement. Technically, Zwei samples a number of trajectories from the same starting point, and instantly estimates the win rate w.r.t the competition outcome. Here the competition result represents which trajectory is closer to the assigned requirement. Subsequently, Zwei optimizes the strategy by maximizing the win rate. To build Zwei, we develop simulation environments, design adequate neural network models, and invent training methods for dealing with different requirements on various video transmission scenarios. Trace-driven analysis over two representative tasks demonstrates that Zwei optimizes itself according to the assigned requirement faithfully, outperforming the state-of-the-art methods under all considered scenarios.

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Section: Background and Challenges 21 Video Transmission Servicesmentioning

confidence: 99%

Self-play reinforcement learning for video transmission

Huang¹,

Zhang²,

Sun³

2020

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video

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“…The use of HAS for over-the-top (OTT) video services has driven researchers for developing new adaptation approaches that optimise the perceived QoE through dynamically adapting the requested quality according to network conditions. Different adaption approaches have presented in the literature [7]. The majority of these approaches are deployed at the end client, which can be broadly classified into throughput-based, buffer-based, and hybrid-based approach.…”

Section: Related Workmentioning

confidence: 99%

BBGDASH: A Max-Min Bounded Bitrate Guidance for SDN Enabled Adaptive Video Streaming

Al-Issa

Bentaleb

Zinner

et al. 2019

2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

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The increase in video traffic and the end-users demands for higher quality video have triggered academia and industry to find novel mechanisms for media distribution. Among the available streaming services, HTTP adaptive streaming (HAS) is being the de facto standard for multi-bitrate streaming. Recent studies show that the bitrate adaptation of client-driven HAS applications is challenging due to the fact that they based on distributed, and local decisions for adapting the quality on a per-client basis. Software-defined networking (SDN) has emerged as a new network paradigm to provide centralised management. The programmability and flexibility of the SDN can be utilised o enhance the delivery of video over the Internet. In this paper, we present a novel and scalable network assistance approach (denoted BBGDASH) that identifies the boundary range of the requested bitrate levels while preserving HAS at the end player. Experimental results demonstrate the potential of the proposed approach for delivering the video over SDN-enabled networks.

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“…Recent surveys [17], [18] give a good overview of the bit rate adaptation algorithms for DASH based content delivery.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks

Vergados

Kralevska

Michalas

et al. 2018

2018 Global Information Infrastructure and Networking Symposium (GIIS)

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Video streaming currently accounts for the majority of Internet traffic. One factor that enables video streaming is HTTP Adaptive Streaming (HAS), that allows the users to stream video using a bit rate that closely matches the available bandwidth from the server to the client. MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a widely used standard, that allows the clients to select the resolution to download based on their own estimations. The algorithm for determining the next segment in a DASH stream is not part of the standard, but it is an important factor in the resulting playback quality. Nowadays vehicles are increasingly equipped with mobile communication devices, and in-vehicle multimedia entertainment systems. In this paper, we evaluate the performance of various DASH adaptation algorithms over a vehicular network. We present detailed simulation results highlighting the advantages and disadvantages of various adaptation algorithms in delivering video content to vehicular users, and we show how the different adaptation algorithms perform in terms of throughput, playback interruption time, and number of interruptions.

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A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

Cited by 349 publications

References 155 publications

Self-play reinforcement learning for video transmission

Self-play reinforcement learning for video transmission

BBGDASH: A Max-Min Bounded Bitrate Guidance for SDN Enabled Adaptive Video Streaming

Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks

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