Eliminating bandwidth estimation from adaptive video streaming in wireless networks

Hwang, Jaehyun; Lee, Jung Hwan

doi:10.1016/j.image.2016.06.013

Cited by 13 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This uses several parameters related to the QoE factor to determine the quality of the segment. WQUAD [29] determines the segment to be requested according to the buffer occupancy level using SVC. The only request to BL is to minimize buffering until the buffer is full.…”

Section: Background and Related Work A Http Adaptive Streamingmentioning

confidence: 99%

Layer-Assisted Video Quality Adaptation for Improving QoE in Wireless Networks

Park

Chung

2020

IEEE Access

View full text Add to dashboard Cite

HTTP adaptive streaming (HAS) is a state-of-the-art technology for video streaming. Optimal adaptive streaming schemes should be designed to maximize the quality of experience (QoE) in network environments with unstable bandwidths such as wireless networks. QoE is degraded for Advanced Video Coding (AVC)-based HAS due to frequent quality switching and rebuffering in wireless networks. Meanwhile, Scalable Video Coding (SVC)-based HAS can maximize the QoE by flexibly requesting the base layer (BL) and enhancement layer (EL) based on variable network conditions. However, in SVC-based HAS, the required bandwidth is significantly high due to the encoding overhead. In this paper, we propose a layer-assisted video quality adaptation for improving QoE in wireless networks. The proposed scheme employs a video encoding method, composed of multiple BLs and ELs, using both AVC and SVC. The quality of the BL is determined using the buffer occupancy level and measured bandwidth to minimize rebuffering. The quality of the EL is determined using the buffer region and segment quality differences to minimize instability. enhancement dummy (ED) consists of the selected ELs. The layer scheduler decides the segment that is to be requested based on the QoE model according to the BL and ED to maximize the QoE. Experimental results show that the proposed scheme achieves high QoE compared to the existing schemes because the instability is low and no rebuffering occurs.INDEX TERMS HTTP adaptive streaming, wireless network, quality of experience, scalable video coding.

show abstract

Section: Background and Related Work A Http Adaptive Streamingmentioning

confidence: 99%

Layer-Assisted Video Quality Adaptation for Improving QoE in Wireless Networks

Park

Chung

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Moreover, Seufert et al 4 described that the segment length needed to be long enough to allow high coding efficiency of the source video encoder. Hwang et al 32 deduced that the segment length needed to be short enough to allow a fast reaction in high network fluctuation. Both requirements need to be considered by content providers during the preparation of adaptive video streaming contents.…”

Section: Related Workmentioning

confidence: 99%

Adaptive video streaming testbed design for performance study and assessment of QoE

Taha

Lloret

Ali

et al. 2018

Int J Communication

View full text Add to dashboard Cite

Hypertext Transfer Protocol adaptive streaming switches between different video qualities, adapting to the network conditions, and avoids stalling streamed frames over high-oscillation client's throughput improving the users' quality of experience (QoE). Quality of experience has become the most important parameter to lead the service providers to know about the end-user feedback. Implementing Hypertext Transfer Protocol adaptive streaming applications to find out QoE in real-life scenarios of vast networks becomes more challenging and complex task regarding to cost, agile, time, and decisions.In this paper, a virtualized network testbed to virtualize various machines to support implementing experiments of adaptive video streaming has been developed. Within the test study, the metrics which demonstrate performance of QoE are investigated, respectively, including initial delay (ie, startup delay at the beginning of playback a video), frequency switches (ie, number of times the quality is changed), accumulative video time (ie, number and length of stalls), CPU usage, and battery energy consumption. Furthermore, the relation between effective parameters of QoS on the aforementioned metrics for different segment length is investigated. Experimental results show that the proposed virtualized system is agile, easy to install and use, and costs less than real testbeds. Moreover, the subjective and objective performance studies of QoE evaluation in the system have proven that the segment lengths of 6 to 8 seconds were faired and more efficient than others according to the investigated parameters.

show abstract

“…For video streaming, Dynamic Adaptive Streaming over HTTP was used in both wired and wireless environments, but video quality oscillation and video freeze in wireless environments cause poor user experience. The Wireless Quality Adaptation (WQUAD) algorithm was developed to provide stable performance [11]. The presented algorithm eliminated bandwidth estimation from quality adaptation and provided video freeze prevention and high video quality on average.…”

Section: Related Workmentioning

confidence: 99%

“…It is clear that the PLR is increasing when the number of users is increasing; however, there is no violation of QoS of video streaming of interfered users using our method. This is due to the power allocation method that tries to decrease the power according to the Equations (7) and (11). The PLR is so high when there is no power allocation as interfered users will experience a very bad channel quality, compared to the utility method.…”

Section: Performance Evaluationmentioning

confidence: 99%

Performance Study of Adaptive Video Streaming in an Interference Scenario of Femto-Macro Cell Networks

Yahiya

Kirci

2018

Information

View full text Add to dashboard Cite

Abstract:The demand for video traffic is increasing over mobile networks that are taking another shape by its heterogeneity. However, the wireless link capacity cannot cope with the traffic demand. This is due to the interference problem that can be considered as the most important challenge in heterogeneous networks. Consequently, it will result in poor service for the quality of video streaming such as the bad quality delivery, service interruption, etc. In this paper, we propose a solution for interference mitigation in the context of heterogeneous networks through power control mechanism, while guaranteeing the Quality of Service of the video streaming. We derive a model for adapting the video bit rate to match the channel's achievable bit rate. Our results demonstrate a high satisfaction for video streaming in terms of delay and throughput.

show abstract

Eliminating bandwidth estimation from adaptive video streaming in wireless networks

Cited by 13 publications

References 26 publications

Layer-Assisted Video Quality Adaptation for Improving QoE in Wireless Networks

Layer-Assisted Video Quality Adaptation for Improving QoE in Wireless Networks

Adaptive video streaming testbed design for performance study and assessment of QoE

Performance Study of Adaptive Video Streaming in an Interference Scenario of Femto-Macro Cell Networks

Contact Info

Product

Resources

About