Quality-of-experience driven adaptive HTTP media delivery

Essaili, Ali El; Schroeder, Damien; Staehle, Dirk; Shehada, Mohammed; Kellerer, Wolfgang; Steinbach, Eckehard

doi:10.1109/icc.2013.6654905

Cited by 70 publications

(34 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rather than focusing on the client controller design, the work in [21] investigates a DASH streaming system over a mobile network where a proxy rewrites client HTTP requests in such a way that the overall QoE experienced by multiple clients is optimized. The work in [21] addresses the main limitations of multiple-clients DASH systems; however it does not address the problem of optimizing the representations on the server and rather seems complementary to our work.…”

Section: Related Workmentioning

confidence: 99%

Optimal Selection of Adaptive Streaming Representations

Toni

Aparicio-Pardo

Pires

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

Adaptive streaming addresses the increasing and heterogeneous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) is characterized by a resolution and a bit rate, and it is aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-buffer control strategies on the client side, in this paper we take a providers' perspective and propose solutions to improve user satisfaction by optimizing the set of available representations. We formulate an integer linear program that maximizes users' average satisfaction, taking into account network dynamics, type of video content, and user population characteristics. The solution of the optimization is a set of encoding parameters corresponding to the representations set that maximizes user satisfaction. We evaluate this solution by simulating multiple adaptive streaming sessions characterized by realistic network statistics, showing that the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness among users and to reduce network resource usage. We finally propose a few theoretical guidelines that can be used, in realistic settings, to choose the encoding parameters based on the user characteristics, the network capacity and the type of video content. Index TermsDynamic adaptive streaming over HTTP, content distribution, video streaming, integer linear program.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimal Selection of Adaptive Streaming Representations

Toni

Aparicio-Pardo

Pires

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

show abstract

“…Different from conventional DASH whose rate adaptation logics are either implemented locally in the user equipment or in the DASH server, WiDASH proxy is in charge of video adaptation, which makes it feasible to perform global optimization over multiple concurrent DASH flows. Another example of resource allocation for DASH downlink streams is proposed in [8] in which an over the top (OTT) approach is used that requires no adaptation of the media content. The advantage of the proposed scheme is obvious in terms of gain in QoE in comparison to both reactive QoEoptimized and to standard-DASH HTTP streaming.…”

Section: B Related Workmentioning

confidence: 99%

QoE-based network-centric resource allocation for on-demand uplink adaptive HTTP streaming over LTE network

Pervez

Raheel

2014

2014 IEEE 8th International Conference on Application of Information and Communication Technologies (AICT)

View full text Add to dashboard Cite

With the advent of community portals and video sharing websites, production and consumption of multimedia content has increased tremendously. It is because of excellent video recording capabilities of cameras available with mobile devices, and enhanced data rates offered by cellular networks, that the uploading of high quality multimedia content is more realistic. Nowadays most of the video traffic is HTTPbased, that provides rate adaptive streaming, known as Dynamic Adaptive Streaming over HTTP (DASH). In this work we generate DASH video streams that can be supported on LTE uplink and then assign resources to the video producers such that the QoE of on-demand viewers is maximized.

show abstract

“…Based on this information and their buffer status, the clients decide the most appropriate quality level to request. El Essaili et al [2013] propose a QoE optimizer for wireless networks that computes the optimal rate for the streaming clients, based on the wireless channel conditions. This value is then used by a QoE proxy, in charge of intercepting and rewriting clients' requests to match the requested quality level with the optimal rate.…”

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.

104

View full text Add to dashboard Cite

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.

show abstract

Quality-of-experience driven adaptive HTTP media delivery

Cited by 70 publications

References 16 publications

Optimal Selection of Adaptive Streaming Representations

Optimal Selection of Adaptive Streaming Representations

QoE-based network-centric resource allocation for on-demand uplink adaptive HTTP streaming over LTE network

QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

Contact Info

Product

Resources

About