Cross-layer scheduling for multi-quality video streaming in cellular wireless networks

Dräxler, Martin; Karl, Holger

doi:10.1109/iwcmc.2013.6583724

Cited by 26 publications

(27 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our evaluation is twofold: First, we compare the optimization problem, the heuristic algorithm and existing algorithms [7] in a small scenario. Second, we compare the heuristic algorithm with an existing algorithm based on the current behavior of the VLC media player [6].…”

Section: Discussionmentioning

confidence: 99%

“…The anticipatory approach for video download [7], [8] has been evaluated before and we base our optimization problem on it. A similar approach with the same system model and a different optimization problem has been proposed by Sadr et al [10].…”

Section: A Related Workmentioning

confidence: 99%

“…In this section we describe the optimization problem, which is based on our previous work [7]. Two schedulers, OptBasic and OptFlex, are implemented with the optimization problem.…”

Section: Optimization Problemmentioning

confidence: 99%

“…• OptLegacy is an existing optimization problem [7] without explicit power cycling. Base stations are initially all switched on and only base stations with no associated users are considered switched off in a post processing step.…”

Section: A Scenariosmentioning

confidence: 99%

“…In our previous work [7], [8] we have shown how a mobile network operator can use such a download schedule to adapt video streaming to changing available data rates for uninterrupted video streaming at a high quality. This approach is based on the prediction [9] of future available data rates in the range of seconds and minutes, which we call anticipation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Anticipatory power cycling of mobile network equipment for high demand multimedia traffic

Dreimann

Karl

2014

2014 IEEE Online Conference on Green Communications (OnlineGreenComm)

Self Cite

View full text Add to dashboard Cite

The increasing energy demand of mobile phone networks is a problem for the environment and a cost factor for mobile network operators. Multimedia content is increasingly popular, demands high data rates, and increases energy usage of mobile phone networks. Thus it is important to reduce energy usage of mobile phone networks while serving multimedia content with a high quality of experience (QoE).We developed an approach to power-cycle base stations and control the playback of video streams while reducing the energy consumption and not decreasing the QoE for users. We present two implementations: an optimization problem and an iterative algorithm.Up to 80% of energy can be saved by combining power cycling and video stream controlling in low-load situations. With increasing load, the energy consumption also increases while the QoE can still be improved.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

“…In this section we describe the optimization problem, which is based on our previous work [7]. Two schedulers, OptBasic and OptFlex, are implemented with the optimization problem.…”

Section: Optimization Problemmentioning

confidence: 99%

Section: A Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Anticipatory power cycling of mobile network equipment for high demand multimedia traffic

Dreimann

Karl

2014

2014 IEEE Online Conference on Green Communications (OnlineGreenComm)

Self Cite

View full text Add to dashboard Cite

show abstract

Q2ABR: QoE‐aware adaptive video bit rate solution

Amour

Souihi

Mellouk

2019

Int J Communication

View full text Add to dashboard Cite

KEYWORDSadaptive bit rate streaming (ABR), breakpoint detection (BPD), boosting gradient regression (GBR), controlled-laboratory, mean opinion score (MOS), machine learning (ML), quality of experience (QoE), reinforcement learning, QoE assessment, streaming video Abbreviations: ABR, adaptive bit rate streaming; BPD, breakpoint detection; GBR, boosting gradient regression; ML, machine learning; MOS, mean opinion score; QoE, quality of experience; RL, reinforcement learning.Int J Commun Syst. 2020;33:e4204.wileyonlinelibrary.com/journal/dac

show abstract

From QoS to QoE: A Tutorial on Video Quality Assessment

Chen

Zhang

2015

IEEE Commun. Surv. Tutorials

289

124

View full text Add to dashboard Cite

Quality of Experience (QoE) is the perceptual Quality of Service (QoS) from the users' perspective. For video service, the relationship between QoE and QoS (such as coding parameters and network statistics) is complicated because users' perceptual video quality is subjective and diversified in different environments. Traditionally, QoE is obtained from subjective test, where human viewers evaluate the quality of tested videos under a laboratory environment. To avoid high cost and offline nature of such tests, objective quality models are developed to predict QoE based on objective QoS parameters, but it is still an indirect way to estimate QoE. With the rising popularity of video streaming over the Internet, data-driven QoE analysis models have newly emerged thanks to availability of large-scale data. In this article, we give a comprehensive survey of the evolution of video quality assessment methods, analyzing their characteristics, advantages and drawbacks. We also introduce QoE-based video applications, and finally identify the future research directions of QoE.Index Terms-Quality of Experience, Subjective Test, Objective Quality Model, Data-driven Analysis 1553-877X (c)

show abstract

Cross-layer scheduling for multi-quality video streaming in cellular wireless networks

Cited by 26 publications

References 11 publications

Anticipatory power cycling of mobile network equipment for high demand multimedia traffic

Anticipatory power cycling of mobile network equipment for high demand multimedia traffic

Q2ABR: QoE‐aware adaptive video bit rate solution

From QoS to QoE: A Tutorial on Video Quality Assessment

Contact Info

Product

Resources

About