Adaptive Bitrate Selection: A Survey

Sani, Yusuf; Mauthe, Andreas; Edwards, Christopher

doi:10.1109/comst.2017.2725241

Cited by 89 publications

(43 citation statements)

References 174 publications

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“…Second, we identify six main open challenges that will need to be addressed by the multimedia delivery community in future years, namely: (1) immersive video streaming, (2) QUIC-based adaptive streaming, (3) video traffic encryption and analysis, (4) personalized QoE modeling and control, (5) video delivery for low-latency, high-mobility applications and (6) open software, testbeds and datasets. The analysis presented in this work complements previous surveys on HAS, which mostly focus on existing rate adaptation heuristics only [50,84,87]. Moreover, this paper goes one step further by giving a structured outline of QoE-centric solutions and an outlook on future directions.…”

Section: Quality Of Experience In Http Adaptive Streaming (Section 2)mentioning

confidence: 74%

“…Since the standard was defined, a large body of research has investigated how to improve users' Quality of Experience (QoE) for HAS [50,84,87]. Many studies focus on the development of new heuristics, in order to improve the quality adaptation of the client [41,57,93].…”

Section: Quality Of Experience In Http Adaptive Streaming (Section 2)mentioning

confidence: 99%

“…We refer to the works by Seufert et al, Kua et al and Sani et al for an exhaustive discussion on HAS rate adaptation heuristics [50,84,87]. As pointed out in Section 1, several QoE factors (as freezes, live latency or fairness) cannot be completely optimized by classical client-based heuristics, which therefore need to be supported by optimizations taking place in the network, at the application or at the transport level.…”

Section: Architecture and Componentsmentioning

confidence: 99%

See 2 more Smart Citations

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Petrangeli

Hooft

Wauters

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

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Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming (HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this paper, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years.

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Section: Quality Of Experience In Http Adaptive Streaming (Section 2)mentioning

confidence: 74%

Section: Quality Of Experience In Http Adaptive Streaming (Section 2)mentioning

confidence: 99%

Section: Architecture and Componentsmentioning

confidence: 99%

See 1 more Smart Citation

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Petrangeli

Hooft

Wauters

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

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“…With this control, traffic demand will decrease when QoS is degraded. Optimizing video bitrate in accordance with QoE with predicted QoS (available bandwidth) has recently gained much attention [34]- [38].…”

Section: Upper-layer Qos Degradation Reactionmentioning

confidence: 99%

Modeling Upper Layer Reaction to QoS Degradation as a Congestion Avoidance Mechanism

Harada

Ishibashi

Kawahara

2020

IEICE Trans. Commun.

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On the Internet, end hosts and network nodes interdependently work to smoothly transfer traffic. Observed traffic dynamics are the result of those interactions among those entities. To manage Internet traffic to provide satisfactory quality services, such dynamics need to be well understood to predict traffic patterns. In particular, some nodes have a function that sends back-pressure signals to backward nodes to reduce their sending rate and mitigate congestion. Transmission Control Protocol (TCP) congestion control in end-hosts also mitigates traffic deviation to eliminate temporary congestion by reducing the TCP sending rate. How these congestion controls mitigate congestion has been extensively investigated. However, these controls only throttle their sending rate but do not reduce traffic volume. Such congestion control fails if congestion is persistent, e.g., for hours, because unsent traffic demand will infinitely accumulate. However, on the actual Internet, even with persistent congestion, such accumulation does not seem to occur. During congestion, users and/or applications tend to reduce their traffic demand in reaction to quality of service (QoS) degradation to avoid negative service experience. We previously estimated that 2% packet loss results in 23% traffic reduction because of this upper-layer reaction [1]. We view this reduction as an upper-layer congestion-avoidance mechanism and construct a closed-loop model of this mechanism, which we call the Upper-Layer Closed-Loop (ULCL) model. We also show that by using ULCL, we can predict the degree of QoS degradation and traffic reduction as an equilibrium of the feedback loop. We applied our model to traffic and packet-loss ratio time series data gathered in an actual network and demonstrate that it effectively estimates actual traffic and packet-loss ratio.

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“…[5]. The general framework of ABR composes of three subcomponents: resources estimation, chunk request scheduling and adaption as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of QoE Deterioration in HTTP Adaptive Streaming

Xuan-Tan¹,

Kamioka²

2017

JCC

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Apart from video rate (or requested bitrate), Mean Opinion Score (MOS) has increasingly become a primary term representing Quality of Experience (QoE) in HTTP adaptive streaming (HAS). By monitoring this metric, QoE management can effectively maximize QoE for the users. However, due to various behaviors of particular commercial HAS players, deciding an appropriate monitoring interval has not been fully investigated yet. In this paper, an optimal interval is proposed to be equal to duration of a video chunk in order to aid service managers in early detecting QoE deterioration and limiting the probability of video rate deterioration. The optimal monitoring interval is evaluated by comparing with other values of interval in terms of ratio of video rate deterioration. Furthermore, MOS-based QoE monitoring method which takes into account the proposed interval is thus compared with video rate based monitoring method. The results show that with optimal interval, MOS monitoring guarantees a low ratio of video rate deterioration (around 10% for buffering state and 40% for steady state) and small average CPU Load (about 11.45%).

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Adaptive Bitrate Selection: A Survey

Cited by 89 publications

References 174 publications

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Modeling Upper Layer Reaction to QoS Degradation as a Congestion Avoidance Mechanism

Early Detection of QoE Deterioration in HTTP Adaptive Streaming

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