Browsing by Author "Harous, S."
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ArticlePublication Metadata only Data-driven bandwidth prediction models and automated model selection for low latency(IEEE, 2021) Bentaleb, A.; Beğen, Ali Cengiz; Harous, S.; Zimmermann, R.; Computer Science; BEĞEN, Ali CengizToday's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate when lower latency is desired due to the shorter time available to react to bandwidth changes, and when mobile networks are involved due to their inherently more frequent and potentially larger bandwidth fluctuations. Any inaccuracy in bandwidth prediction results in flawed adaptation decisions, which will in turn translate into a diminished viewer experience. We propose an Automated Model for Prediction (AMP) that encompasses techniques for bandwidth prediction and model auto-selection specifically designed for low-latency live steaming with chunked transfer encoding. We first study statistical and computational intelligence techniques to implement a suite of bandwidth prediction models that can work accurately under a broad range of network conditions, and second, we introduce an automated prediction model selection method. We confirm the effectiveness of our solution through trace-driven live streaming experiments.Conference paperPublication Metadata only A distributed approach for bitrate selection in HTTP adaptive streaming(ACM, 2018) Bentaleb, A.; Beğen, Ali Cengiz; Harous, S.; Zimmermann, R.; Computer Science; BEĞEN, Ali CengizPast research has shown that concurrent HTTP adaptive streaming (HAS) players behave selfishly and the resulting competition for shared resources leads to underutilization or oversubscription of the network, presentation quality instability and unfairness among the players, all of which adversely impact the viewer experience. While coordination among the players, as opposed to all being selfish, has its merits and may alleviate some of these issues. A fully distributed architecture is still desirable in many deployments and better reflects the design spirit of HAS. In this study, we focus on and propose a distributed bitrate adaptation scheme for HAS that borrows ideas from consensus and game theory frameworks. Experimental results show that the proposed distributed approach provides significant improvements in terms of viewer experience, presentation quality stability, fairness and network utilization, without using any explicit communication between the players.ArticlePublication Metadata only Game of streaming players: Is consensus viable or an illusion?(Association for Computing Machinery, Inc, 2019-08) Bentaleb, A.; Beğen, Ali Cengiz; Harous, S.; Zimmermann, R.; Computer Science; BEĞEN, Ali CengizThe dramatic growth of HTTP adaptive streaming (HAS) traffic represents a practical challenge for service providers in satisfying the demand from their customers. Achieving this in a network where multiple players share the network capacity has so far proved hard because of the bandwidth competition among the HAS players. This competition is exacerbated by the bandwidth overestimation that is introduced due to the isolated and selfish behavior of the HAS players. Each player strives individually to select the maximum bitrate without considering the co-existing players or network resource dynamics. As a result, the HAS players suffer from video quality instability, quality unfairness, and network underutilization or oversubscription, and the players observe a poor quality of experience (QoE). To address this issue, we propose a fully distributed game theory and consensus-based collaborative adaptive bitrate solution for shared network environments, termed Game Theory and consensus-based Approach for Cooperative HAS delivery systems (GTAC). Our solution consists of two-stage games that run in parallel during a streaming session. We extensively evaluate GTAC on a broad set of trace-driven and real-world experiments. Results show that GTAC enhances the viewer QoE by up to 22%, presentation quality stability by up to 24%, fairness by at least 31%, and network utilization by 28% compared to the well-known schemes.ArticlePublication Metadata only SDNHAS: An SDN-Enabled architecture to optimize qoe in http adaptive streaming(IEEE, 2017-10) Bentaleb, A.; Beğen, Ali Cengiz; Zimmermann, R.; Harous, S.; Computer Science; BEĞEN, Ali CengizHTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture called SDNDASH [1] to address HAS scalability issues including video instability, quality of experience (QoE) unfairness, and network resource underutilization, while maximizing per player QoE. While SDNDASH was a significant step forward, there were three unresolved limitations: 1) it did not scale well when the number of HAS players increased; 2) it generated communication overhead; and 3) it did not address client heterogeneity. These limitations could result in suboptimal decisions that led to viewer dissatisfaction. To that effect, we propose an enhanced intelligent streaming architecture, called SDNHAS, which leverages software defined networking (SDN) capabilities of assisting HAS players in making better adaptation decisions. This architecture accommodates large-scale deployments through a cluster-based mechanism, reduces communication overhead between the HAS players and SDN core, and allocates the network resources effectively in the presence of short- and long-term changes in the network.Conference paperPublication Metadata only Want to play DASH?: a game theoretic approach for adaptive streaming over HTTP(Association for Computing Machinery, Inc, 2018-06-12) Bentaleb, A.; Beğen, Ali Cengiz; Harous, S.; Zimmermann, R.; Computer Science; BEĞEN, Ali CengizIn streaming media, it is imperative to deliver a good viewer experience to preserve customer loyalty. Prior research has shown that this is rather difficult when shared Internet resources struggle to meet the demand from streaming clients that are largely designed to behave in their own self-interest. To date, several schemes for adaptive streaming have been proposed to address this challenge with varying success. In this paper, we take a different approach and develop a game theoretic approach. We present a practical implementation integrated in the dash.js reference player and provide substantial comparisons against the state-of-the-art methods using trace-driven and real-world experiments. Our approach outperforms its competitors in the average viewer experience by 38.5% and in video stability by 62%.