Video Splicing Techniques for P2P Video Streaming

Islam, Iftakharul; Khan, Javed

doi:10.1109/icdcsw.2015.23

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…They deduced that downloading shorter segments prohibits the TCP's sending window from reaching high values, which causes the adaptation logic to remain at lower quality levels. Islam and Khan [15] observed that downloading one large segment is faster than downloading multiple smaller segments; they considered the option that, instead of switching to a lower bitrate version, the segment duration could be varied in insufficient network conditions. In addition, Liu et al [16] studied the possibility of using segment duration in the rate adaptation.…”

Section: Related Workmentioning

confidence: 99%

Network Capacity Estimators Predicting QoE in HTTP Adaptive Streaming

Laine

Hakala

2022

IEEE Access

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The aim of adaptive HTTP streaming technology is preserving the best possible video streaming quality for viewers in heterogeneous network conditions. This can be achieved by making multiple quality versions of the video available. Switching between versions during playback should be imperceptible and fluent. The decision about quality-level switching is typically based on network capacity estimation and buffer occupancy, which predict the risk of stalling. Since quality-level switching and stalling are directly evident to the user, they are often classified as influence factors of quality of experience (QoE). In this paper, we observe different network capacity estimators and buffer behavior in limited network conditions and study how the estimators predict QoE. The challenges of variable bitrate (VBR)-encoded video are considered. We also propose two new estimators to predict QoE. One compares segment fetch time to segment playback time, while the other explores the difference of throughput and average download rate. As segment duration may influence HTTP adaptive streaming (HAS) playback in unstable conditions, the findings are tested with four segment lengths. Moreover, streaming quality is analyzed in a testbed using two popular web players to reveal possible effects of the players' features.

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Section: Related Workmentioning

confidence: 99%

Network Capacity Estimators Predicting QoE in HTTP Adaptive Streaming

Laine

Hakala

2022

IEEE Access

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A generic framework for application streaming service

Malik

Rasool

Anwar

et al. 2018

Computers & Electrical Engineering

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A Multi-Objective Video Crowdsourcing Method in Mobile Environment

et al. 2019

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With the rapid development of mobile video services, HD and UHD videos are attractive for mobile users due to the realistic visual enjoyment and the accurate representation. However, the limited transmission bit rate in 4G communication network affects the experience of the users for watching videos. Crowdsourcing is considered as a reasonable and effective solution to alleviate the resource limitation. Through employing the crowdsourcing participants to download and transmit video segments, mobile users can get enhanced video services. However, it is still a significant challenge that how to avoid excessive payment and energy consumption when the crowdsourcing participants download the video segments for the mobile users. To address this challenge, a multi-objective video crowdsourcing method in mobile environment is proposed in this paper. Technically, the crowdsourcing participants apply device-to-device (D2D) communication technique rather than the cellular network or bluetooth transmission to transmit video segments to the mobile users. Here, we divide our problem into two situations, the single participant case and the multi-participants case. In the single participant case, we apply the improved dynamic programming algorithm to find strategies with more enhanced video service time that the crowdsourcing participants provide for the mobile users. Then Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multiple Criteria Decision Making (MCDM) techniques are applied to find a balanced strategy to maximize the enhanced video service time and minimize the payment and the energy consumption. In the multi-participants case, through DBSCAN clustering, the problem with multi-participants is divided into several problems with single participant. Finally, extensive experimental evaluations are conducted to demonstrate the effectiveness and efficiency of our proposed method.

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Video Splicing Techniques for P2P Video Streaming

Cited by 3 publications

References 10 publications

Network Capacity Estimators Predicting QoE in HTTP Adaptive Streaming

Network Capacity Estimators Predicting QoE in HTTP Adaptive Streaming

A generic framework for application streaming service

A Multi-Objective Video Crowdsourcing Method in Mobile Environment

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