XMAS: An Efficient Mobile Adaptive Streaming Scheme Based on Traffic Shaping

Kim, Seohyang; Kim, Chong-kwon

doi:10.1109/tmm.2018.2856626

Cited by 21 publications

(8 citation statements)

References 52 publications

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“…QUETRA determines the optimal quality by calculating the expected buffer occupancy using the video bitrate, network throughput and buffer occupancy [17]. XMAS employs a client-side traffic shaping technique to limit QoE degradation, which arises due to the ON-OFF segment request pattern in multiple HAS client environments; and consequently improves the HAS QoE [18]. SATE is proposed as a HAS that uses a QoE based objective function to reduce the quality switching frequency and to respond quickly to bandwidth changes [19].…”

Section: A Http Adaptive Streamingmentioning

confidence: 99%

Dynamic Block-Based HTTP Adaptive Streaming Scheme in Multiple Wireless Networks

2020

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Multipath-based hypertext transfer protocol (HTTP) adaptive streaming (MPHAS) has been studied due to the increasing use of mobile devices equipped with multiple network interfaces and the prevalence of high-quality video streaming services. However, the existing MPHAS schemes undergo a degraded quality of experience (QoE) in multiple wireless networks. The segment request-based MPHAS (SR-MPHAS) encounters frequent quality switching, and the block request-based MPHAS (BR-MPHAS) causes results in playback stalling and inefficient bandwidth utilization, according to slow responsiveness. In this paper, we propose a dynamic block-based MPHAS (DBAS) to improve the QoE by overcoming the limitations of the existing schemes in multiple wireless networks. We propose buffer-based block length adaptation and QoE-based block quality adaptation to reduce the frequency of quality switching and to improve the responsiveness to network changes. Moreover, we propose a partial request-based block scheduler to mitigate segment reordering. The experimental results show that the DBAS improves the QoE by reducing the quality switching frequency and rapidly adapting to abrupt changes in the bandwidth.INDEX TERMS HTTP adaptive streaming, multipath-based transmission, multiple wireless networks, quality of experience.

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Section: A Http Adaptive Streamingmentioning

confidence: 99%

Dynamic Block-Based HTTP Adaptive Streaming Scheme in Multiple Wireless Networks

2020

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“…(3) Many studies [5], [9], [20] show that in the condition of multiple clients sharing a bottleneck bandwidth, a client with higher bitrate always detects higher bandwidth in a simple playback environment. Here, as H x [n] is designed to be negatively related to ϕ x and Qr x [n], it will weakens the bandwidth overestimation of the client with higher request data weighted by ϕ x than the average request data weighted by ϕ x of all…”

Section: A Server-side Cooperation Assistancementioning

confidence: 99%

“…Some researchers committed to achieve more precise bandwidth estimation in the client-side because studies show that the ON-OFF pattern (adaptive streaming uses network intermittently because of multiple segment-wise timed downloads, which causes the ON-OFF pattern during streaming.) of ABR algorithm leads to inaccurate bandwidth estimation [9]- [12]. Some studies dedicated to only use a buffer to complete the bitrate selection [13]- [17].…”

Section: Introductionmentioning

confidence: 99%

PREPARE-Playback RatE and Priority Adaptive BitRate Selection

et al. 2019

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HAS (HTTP-based Adaptive Streaming) based on ABR (Adaptive Bitrate Rate selection) has become a prevalent solution to media transmission. In order to improve the performance of HAS, many studies focus on how to reduce the unfairness, the instability and the inefficiency in a multi-clients scenario. However, the unfairness problem of the multi-clients with different priority levels and different playback rates, and the instability problem caused by the changes of the playback rate are still unresolved. To solve these problems, a novel ABR algorithm called PREPARE is proposed in this paper. In addition to normally considered throughput and client buffer information in HAS system model, the client priority and the playback rate are also incorporated to make the system model more accurate. With the feedback from servers, PREPARE algorithm is able to estimate the bandwidth much precisely and to reduce the unfairness among the multi-clients. The stability in bitrate selection is improved by introducing the buffer status in conjunction with the real-time playback rate. Moreover, the buffer underflow is prevented in download scheduling by advancing download according to the real-time playback rate. NS-3 simulator is used to evaluate the performance of PREPARE. The performance results show that PREPARE reduces the unfairness among multi-clients by more than 42% comparing to other three algorithms including ESTC, PANDA, FDASH. Meanwhile, PREPARE simultaneously keeps high stability and high efficiency. INDEX TERMS Streaming media, ABR, bitrate, bandwidth estimation. RUIXING SONG received the B.S. degree in communication engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2014. She is currently pursuing the Ph.D. degree in signal and information processing with the

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“…The existing work related to rate adaptation can be mainly divided into two categories. The first is the research of adaptive bit rate (ABR) algorithms over application layer, including [8][9][10][11]. They usually use HTTP as the transport protocol and adjust the video bit rate according to bandwidth estimation and buffer state [9].…”

Section: Introductionmentioning

confidence: 99%

“…The first is the research of adaptive bit rate (ABR) algorithms over application layer, including [8][9][10][11]. They usually use HTTP as the transport protocol and adjust the video bit rate according to bandwidth estimation and buffer state [9]. However, the underlying TCP of HTTP will essentially bring high latency [12], which is not suitable for real-time video transmission.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning Based Congestion Control for Real-Time Video Communication

Dai

Zhang

et al. 2020

IEEE Trans. Multimedia

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With the increasing demands on interactive video applications, how to adapt video bit rate to avoid network congestion has become critical, since congestion results in selfinflicted delay and packet loss which deteriorate the quality of real-time video service. The existing congestion control is hard to simultaneously achieve low latency, high throughput, good adaptability and fair bandwidth allocation, mainly because of the hardwired control strategy and egocentric convergence objective.To address these issues, we propose an end-to-end statistical learning based congestion control, named Iris. By exploring the underlying principles of self-inflicted delay, we reveal that congestion delay is determined by sending rate, receiving rate and network status, which inspires us to control video bit rate using a statistical-learning congestion control model. The key idea of Iris is to force all flows to converge to the same queue load, and adjust the bit rate by the model. All flows keep a small and fixed number of packets queuing in the network, thus the fair bandwidth allocation and low latency are both achieved. Besides, the adjustment step size of sending rate is updated by online learning, to better adapt to dynamically changing networks.We carried out extensive experiments to evaluate the performance of Iris, with the implementations of transport layer (UDP) and application layer (QUIC) respectively. The testing environment includes emulated network, real-world Internet and commercial LTE networks. Compared against TCP flavors and state-of-the-art protocols, Iris is able to achieve high bandwidth utilization, low latency and good fairness concurrently. Especially over QUIC, Iris is able to increase the video bitrate up to 25%, and PSNR up to 1dB.Index Terms-Congestion control, real-time video streaming, low latency, statistical learning, adaptive adjustment.• Non-coexistence of high throughput and low latency.Considering only packet loss will lead to high queuing delay, but the algorithms overly concerned about delay also lead to low throughput [25]. • Hardwired control strategy. Most methods adjust sending rate with fixed step size or multiplier [26]. This manual mapping cannot always be optimal in changing networks, resulting in performance degradation. • Egocentric convergence objective. Some algorithms based on objective function are self-centered and lack communication between concurrent data streams, thus thye can not keep the same convergence goal for different clients, which leads to unfair bandwidth allocation.Motivated by these issues, to obtain higher video transmission quality, we probe into the essence of congestion control and consider whether it is possible to design an algorithm that achieves the goals of low latency, high channel utilization, good adaptability to changing networks and fair bandwidth allocation. Some recently proposed algorithms [27, 28] have enlightened us: a congestion control with low-latency fairness arXiv:1905.05998v2 [cs.MM]

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XMAS: An Efficient Mobile Adaptive Streaming Scheme Based on Traffic Shaping

Cited by 21 publications

References 52 publications

Dynamic Block-Based HTTP Adaptive Streaming Scheme in Multiple Wireless Networks

Dynamic Block-Based HTTP Adaptive Streaming Scheme in Multiple Wireless Networks

PREPARE-Playback RatE and Priority Adaptive BitRate Selection

Statistical Learning Based Congestion Control for Real-Time Video Communication

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