Statistically Indifferent Quality Variation: An Approach for Reducing Multimedia Distribution Cost for Adaptive Video Streaming Services

Rainer, Benjamin; Petscharnig, Stefan; Timmerer, Christian; Hellwagner, Hermann

doi:10.1109/tmm.2016.2629761

Cited by 28 publications

(18 citation statements)

References 16 publications

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“…Our study differs from it in that we consider the data usage and quality tradeoffs when streaming a video. The study in [40] observes that, for some chunks, lower bitrate tracks may be of similar perceptual quality as higher bitrate tracks. Given a set of encoded ABR tracks, it proposes to perform a server-side chunk replacement (within the same resolution) so that a higher bitrate chunk can be replaced by a lower bitrate chunk with a perceptually similar quality.…”

Section: Related Workmentioning

confidence: 96%

Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

Qin¹,

Hao

Pattipati³

et al. 2019

Proceedings of the 10th ACM Multimedia Systems Conference

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Streaming videos over cellular networks is highly challenging. Since cellular data is a relatively scarce resource, many video and network providers offer options for users to exercise control over the amount of data consumed by video streaming. Our study shows that existing data saving practices for Adaptive Bitrate (ABR) videos are suboptimal: they often lead to highly variable video quality and do not make the most effective use of the network bandwidth. We identify underlying causes for this and propose two novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. We implement and integrate our solutions into two video player platforms (dash.js and ExoPlayer), and conduct thorough evaluations over emulated/commercial cellular networks using real videos. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.

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Section: Related Workmentioning

confidence: 96%

Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

Qin¹,

Hao

Pattipati³

et al. 2019

Proceedings of the 10th ACM Multimedia Systems Conference

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show abstract

“…One major challenge regarding video streaming is the lack of a unified quantitative approach to measure the QoE. Existing HAS solutions in industry and academia assess their QoE based on three different metrics: (1) Objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) [40], [41], Structural SIMilarity (SSIM and SSIMplus) [31], [42], Perceived Video Quality (PVQ) [43], and Statistically Indifferent Quality Variation (SIQV) [44]; (2) Subjective metrics, such as Mean Opinion Score (MOS); or (3) Qualityof-Service (QoS)-derived metrics such as the startup delay, average video bitrate, quality switches and rebuffering events. Achieving high QoE is difficult because trying to optimize each metric may result in conflicts.…”

Section: B Common Problems In Http Adaptive Streamingmentioning

confidence: 99%

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

Bentaleb

Taani

Begen

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

349

174

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In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive streaming (HAS). As a key distinction from other streaming approaches, the bitrate adaptation algorithms in HAS are chiefly executed at each client, i.e., in a distributed manner. The objective of these algorithms is to ensure a high quality of experience (QoE) for viewers in the presence of bandwidth fluctuations due to factors like signal strength, network congestion, network reconvergence events, etc. While such fluctuations are common in public Internet, they can also occur in home networksor even managed networks where there is often admission control and QoS tools. Bitrate adaptation algorithms may take factors like bandwidth estimations, playback buffer fullness, device features, viewer preferences, and content features into account, albeit with different weights. Since the viewer's QoE needs to be determined in real-time during playback, objective metrics are generally used including number of buffer stalls, duration of startup delay, frequency and amount of quality oscillations, and video instability. By design, the standards for HAS do not mandate any particular adaptation algorithm, leaving it to system builders to innovate and implement their own method. This survey provides an overview of the different methods proposed over the last several years.

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“…where Inequality (7) derives from the decreasing pace of c, and Inequality (8) derives from the fact that some data at the end can be transmitted beforehand. On the other hand, we have…”

Section: A the Threshold Scheme For Transmission Schedulementioning

confidence: 99%

NEWCAST: Joint Resource Management and QoE-Driven Optimization for Mobile Video Streaming

Triki¹,

El-Azouzi²,

Haddad³

2020

IEEE Trans. Netw. Serv. Manage.

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Statistically Indifferent Quality Variation: An Approach for Reducing Multimedia Distribution Cost for Adaptive Video Streaming Services

Cited by 28 publications

References 16 publications

Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

NEWCAST: Joint Resource Management and QoE-Driven Optimization for Mobile Video Streaming

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