Understanding Video Streaming Algorithms in the Wild

Licciardello, Melissa; Grüner, Maximilian; Singla, Ankit

doi:10.1007/978-3-030-44081-7_18

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…One may analyze Javascript code [58] or use manual experimentation [59] to understand the behavior of client-side ABR algorithms. A different approach is to model the external behavior of ABR algorithms without inspecting their internal workings.…”

Section: Black-box Modeling Of Video Playersmentioning

confidence: 99%

CANE: A Cascade-Control Approach for Network-Assisted Video QoE Management

Hosseinzadeh¹,

Shankar²,

Apostolaki³

et al. 2023

Preprint

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Prior efforts have shown that network-assisted schemes can improve the Quality-of-Experience (QoE) and QoE fairness when multiple video players compete for bandwidth. However, realizing network-assisted schemes in practice is challenging, as: i) the network has limited visibility into the client players' internal state and actions; ii) players' actions may nullify or negate the network's actions; and iii) the players' objectives might be conflicting. To address these challenges, we formulate network-assisted QoE optimization through a cascade control abstraction. This informs the design of CANE, a practical network-assisted QoE framework. CANE uses machine learning techniques to approximate each player's behavior as a black-box model and model predictive control to achieve a near-optimal solution. We evaluate CANE through realistic simulations and show that CANE improves multiplayer QoE fairness by ∼50% compared to pure client-side adaptive bitrate algorithms and by ∼20% compared to uniform traffic shaping.

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Section: Black-box Modeling Of Video Playersmentioning

confidence: 99%

CANE: A Cascade-Control Approach for Network-Assisted Video QoE Management

Hosseinzadeh¹,

Shankar²,

Apostolaki³

et al. 2023

Preprint

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“…We believe that these elements can again be exploited to enrich the data, which can be subsequently used to produce more realistic and detailed evaluations. For instance, real-world traces were used for understanding the different behaviors of adaptive video streaming algorithms [48].…”

Section: Passive and Self Measurementsmentioning

confidence: 99%

Measurement-driven design and runtime optimization in edge computing: Methodology and tools

et al. 2021

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“…Some of them focus on the presence of IoT devices on the same spectrum, others optimize for energy-efficiency. In [92], they perform an experimental analysis of 10 widely deployed ABRs. Their measurement shows none of the deployed ABRs focus on available bandwidth and some leave a large fraction of available network capacity unused.…”

Section: F Video Stream In Vehicular Networkmentioning

confidence: 99%

Adaptive Bitrate Video Streaming for Wireless nodes: A Survey

Nishat,

Gnawali,

Abdelhadi

2020

Preprint

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In today's Internet, video is the most dominant application and in addition to this, wireless networks such as WiFi, Cellular, and Bluetooth have become ubiquitous. Hence, most of the Internet traffic is video over wireless nodes. There is a plethora of research to improve video streaming to achieve high Quality of Experience (QoE) over the Internet. Many of them focus on wireless nodes. Recent measurement studies often show QoE of video suffers in many wireless clients over the Internet. Recently, many research papers have presented models and schemes to optimize the Adaptive BitRate (ABR) based video streaming for wireless and mobile users. In this survey, we present a comprehensive overview of recent work in the area of Internet video specially designed for wireless network. Recent research has suggested that there are some new challenges added by the connectivity of clients through wireless. Also these challenges become more difficult to handle when these nodes are mobile. This survey also discusses new potential areas of future research due to the increasing scarcity of wireless spectrum.

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Understanding Video Streaming Algorithms in the Wild

Cited by 12 publications

References 21 publications

CANE: A Cascade-Control Approach for Network-Assisted Video QoE Management

CANE: A Cascade-Control Approach for Network-Assisted Video QoE Management

Measurement-driven design and runtime optimization in edge computing: Methodology and tools

Adaptive Bitrate Video Streaming for Wireless nodes: A Survey

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