An evaluation of TCP-based rate-control algorithms for adaptive internet streaming of H.264/SVC

Kuschnig, Robert; Kofler, Ingo; Hellwagner, Hermann

doi:10.1145/1730836.1730856

Cited by 73 publications

(23 citation statements)

References 14 publications

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“…This solution is investigated by De Cicco et al [2011] and Kuschnig et al [2010]. De Cicco et al [2011] propose a control theory-based algorithm to control the length of the sender buffer, placed at the server, in order to select the most appropriate quality level.…”

Section: Related Workmentioning

confidence: 99%

“…De Cicco et al [2011] propose a control theory-based algorithm to control the length of the sender buffer, placed at the server, in order to select the most appropriate quality level. In the work presented by Kuschnig et al [2010], the server estimates the bandwidth of the TCP connection considering the number of bytes transmitted in a time unit. This estimation is performed on a per-GOP (Group of Pictures) basis.…”

Section: Related Workmentioning

confidence: 99%

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QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

Petrangeli

Famaey

Claeys

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

102

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HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today's heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients' Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80% compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time. Latré, Middelheimlaan 1, B-2020 Antwerp, Belgium; emails: {jeroen.famaey, steven.latre}@uantwerpen.be. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

Petrangeli

Famaey

Claeys

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

102

View full text Add to dashboard Cite

show abstract

“…Another important class of adaptation algorithms (see Figure 1(b)) employs scalable codecs such as H264/MPEG-4 AVC [9,10]. Both spatial and temporal scalability can be exploited to adapt picture resolution and frame rate without having to re-encode the raw video content.…”

Section: Adaptive Streaming Techniquesmentioning

confidence: 99%

Feedback control for adaptive live video streaming

Cicco

Mascolo

Palmisano

2011

Proceedings of the Second Annual ACM Conference on Multimedia Systems

192

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Multimedia content feeds an ever increasing fraction of the Internet traffic. Video streaming is one of the most important applications driving this trend. Adaptive video streaming is a relevant advancement with respect to classic progressive download streaming such as the one employed by YouTube. It consists in dynamically adapting the content bitrate in order to provide the maximum Quality of Experience, given the current available bandwidth, while ensuring a continuous reproduction. In this paper we propose a Quality Adaptation Controller (QAC) for live adaptive video streaming designed by employing feedback control theory. An experimental comparison with Akamai adaptive video streaming has been carried out. We have found the following main results: 1) QAC is able to throttle the video quality to match the available bandwidth with a transient of less than 30s while ensuring a continuous video reproduction; 2) QAC fairly shares the available bandwidth both in the cases of a concurrent TCP greedy connection or a concurrent video streaming flow; 3) Akamai underutilizes the available bandwidth due to the conservativeness of its heuristic algorithm; moreover, when abrupt available bandwidth reductions occur, the video reproduction is affected by interruptions.

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“…However, changing the rate too often or by too much may adversely affect the users' quality of experience [3]. Increasingly, web-services run variants of HTTP over TCP for media delivery [15], [16], [17] but TCP can only be used within strict bounds of network characteristics [18] which may be difficult to adhere to in a 3G environment.…”

Section: Related Workmentioning

confidence: 99%

Predictive buffering for streaming video in 3G networks

Singh

Ott

Curcio

2012

2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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Abstract-This paper presents a multimedia streaming service in a mobile (3G) environment that, in addition to in-band congestion signals such as packet losses and delay variations, receives congestion cues from a Network Coverage Map Service (NCMS) to make rate-control decisions. The streaming client routinely queries the NCMS to assess the network conditions at future locations along its expected path. The streaming client may ask the streaming server for short-term transmission bursts to increase pre-buffering when it is approaching areas with bad network performance to maintain media quality. If needed, the client may also switch to a different encoding rate (rate-switching) depending on the severity of expected congestion. These notifications are scheduled as late as possible, so that any changes in network conditions and/or changes in user's movements can be taken into account (late scheduling). Using this type of geo-predictive media streaming service we show that the streaming client can provide pause-less playback and better quality of experience to the user.

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An evaluation of TCP-based rate-control algorithms for adaptive internet streaming of H.264/SVC

Cited by 73 publications

References 14 publications

QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

Feedback control for adaptive live video streaming

Predictive buffering for streaming video in 3G networks

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