SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP

Sani, Yusuf; Raca, Darijo; Quinlan, Jason J.; Sreenan, Cormac J.

doi:10.1109/qomex48832.2020.9123139

Cited by 14 publications

(12 citation statements)

References 17 publications

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“…We use video logs generated by experiments in [17] for the creation of the video sequences. The video logs are generated based on bandwidth traces collected from real operational networks.…”

Section: Video Logs Generationmentioning

confidence: 99%

“…For traffic shaping, bandwidth logs were used from three different wireless technologies, 3G, 4G and WiFi, including various mobility patterns (static, pedestrian, bus, car and tram). Table 3 shows summary statistics (average and standard deviation of measured bandwidth) for these logs [17]. 4 and sourced from [12].…”

Section: Video Logs Generationmentioning

confidence: 99%

“…Motivated by this observation, we offer a framework for creating video sequences based on video logs collected either in real network or based on realistic bandwidth traces. Furthermore, we provide 234 video sequences based on video logs analysed over different bandwidth profiles collected from various wireless networks [17]. Video logs were generated by HAS streaming algorithms under bandwidth profiles from different networks, resulting in a realistic snapshot of decisions algorithms made, including bitrate decisions (giving us rate distribution) and stall events (number and duration of stalls).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Realistic Video Sequences for Subjective QoE Analysis

Hodžić¹,

Cosovic²,

Mrdović³

et al. 2022

Preprint

Self Cite

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Multimedia streaming over the Internet (live and on demand) is the cornerstone of modern Internet carrying more than 60% of all traffic. With such high demand, delivering outstanding user experience is a crucial and challenging task. To evaluate user Quality of Experience (QoE) many researchers deploy subjective quality assessments where participants watch and rate videos artificially infused with various temporal and spatial impairments. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open-source framework for re-creating adaptively streamed video in real networks. DashReStreamer utilises a log created by a HTTP adaptive streaming (HAS) algorithm run in an uncontrolled environment (i.e., wired or wireless networks), encoding visual changes and stall events in one video file. These videos are applicable for subjective QoE evaluation mimicking realistic network conditions.To supplement DashReStreamer, we re-create 234 realistic video clips, based on video logs collected from real mobile and wireless networks. In addition our dataset contains both video logs with all decisions made by the HAS algorithm and network bandwidth profile illustrating throughput distribution. We believe this dataset and framework will permit other researchers in their pursuit for the final frontier in understanding the impact of video QoE dynamics.

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Section: Video Logs Generationmentioning

confidence: 99%

Section: Video Logs Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Realistic Video Sequences for Subjective QoE Analysis

Hodžić¹,

Cosovic²,

Mrdović³

et al. 2022

Preprint

Self Cite

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“…Previous research states that bitrate depends upon ABR models having trained predictive collection of decisions (SMASH [13]). Where a 'combine grouping' scheme was used to make a map network-related properties of bitrate.…”

Section: Problem Space and Related Workmentioning

confidence: 99%

Quality of Experience Experimentation Prediction Framework Through Programmable Network Management

Basil¹,

Mu²,

Al-Sharbaz³

2022

Preprint

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Quality of Experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the QoS. In this paper we examine the Adaptive streaming over a software defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and network. This was done to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE.

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“…In [130], Yusuf et al proposed SMASH: a supervised machine learning method for adaptive streaming over HTTP. This work, like Hiba et al's work, was an attempt to develop a more generalized method to ABR selection.…”

Section: Video Quality Prediction Under Qod Impairmentsmentioning

confidence: 99%

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

2021

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A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

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SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP

Cited by 14 publications

References 17 publications

Realistic Video Sequences for Subjective QoE Analysis

Realistic Video Sequences for Subjective QoE Analysis

Quality of Experience Experimentation Prediction Framework Through Programmable Network Management

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

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