Browsing by Author "Zimmermann, R."

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Bandwidth prediction in low-latency chunked streaming
(The ACM Digital Library, 2019-06) Bentaleb, A.; Timmerer, C.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz
HTTP adaptive streaming with chunked transfer encoding can be used to offer low-latency streaming without sacrificing the coding efficiency. While this allows a media segment to be generated and delivered at the same time, which is critical in reducing the latency, the conventional bitrate adaptation schemes make often grossly inaccurate bandwidth measurements due to the presence of idle periods between the chunks. These wrong measurements cause the streaming client to make bad adaptation decisions. To this end, we design ACTE, a new bitrate adaptation scheme that leverages the unique nature of chunk downloads. ACTE uses a sliding window to accurately measure the available bandwidth and an online linear adaptive filter to predict the bandwidth into the future. Results show that ACTE achieves 96% measurement accuracy, which translates to a 65% reduction in the number of stalls and a 49% increase in quality of experience on average compared to other schemes.
Open Access
Bandwidth prediction in low-latency media transport
(ACM, 2023-06-16) Bentaleb, A.; Akçay, Mehmet Necmettin; Lim, M.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz; Akçay, Mehmet Necmettin
Designing a robust bandwidth prediction algorithm for low-latency media transport that can quickly adapt to varying network conditions is challenging. In this paper, we present the working principles of a hybrid bandwidth predictor (termed BoB, Bang-on-Bandwidth) we developed recently for real-time communications and discuss its use with the new Media-over-QUIC (MOQ) protocol proposals.
Open Access
The benefits of server hinting when DASHing or HLSing
(ACM, 2022-03-17) Lim, M.; Akçay, Mehmet Necmettin; Bentaleb, A.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz; Akçay, Mehmet Necmettin
Streaming clients almost always compete for the available bandwidth and server capacity. Not every client's playback buffer conditions will be the same, though, nor should be the priority with which the server processes the individual requests coming from these clients. In an earlier work, we demonstrated that if clients conveyed their buffer statuses to the server using a Common Media Client Data (CMCD) query argument, the server could allocate its output capacity among all the requests more wisely, which could significantly reduce the rebufferings experienced by the clients. In this paper, we address the same problem using the Common Media Server Data (CMSD) standard that is work-in-progress at the Consumer Technology Association (CTA). In this case, the incoming requests are scheduled based on their CMCD information. For example, the response to a request indicating a healthy buffer status is held/delayed until more urgent requests are handled. When the delayed response is eventually transmitted, the server attaches a new CMSD parameter to indicate how long the delay was. This parameter avoids misinterpretations and subsequent miscalculations by the client's rate-adaptation logic. We implemented the server and client understanding/processing CMCD and CMSD, respectively. Our experiments show that the proposed CMSD parameter effectively eliminates unnecessary downshifting while reducing both the rebuffering rate and duration.
Open Access
BoB: Bandwidth prediction for real-time communications using heuristic and reinforcement learning
(IEEE, 2023) Bentaleb, A.; Akçay, Mehmet Necmettin; Lim, M.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz; Akçay, Mehmet Necmettin
Bandwidth prediction is critical in any Real-time Communication (RTC) service or application. This component decides how much media data can be sent in real time. Subsequently, the video and audio encoder dynamically adapts the bitrate to achieve the best quality without congesting the network and causing packets to be lost or delayed. To date, several RTC services have deployed the heuristic-based Google Congestion Control (GCC), which performs well under certain circumstances and falls short in some others. In this paper, we leverage the advancements in reinforcement learning and propose BoB (Bang-on-Bandwidth) — a hybrid bandwidth predictor for RTC. At the beginning of the RTC session, BoB uses a heuristic-based approach. It then switches to a learning-based approach. BoB predicts the available bandwidth accurately and improves bandwidth utilization under diverse network conditions compared to the two winning solutions of the ACM MMSys'21 grand challenge on bandwidth estimation in RTC. An open-source implementation of BoB is publicly available for further testing and research.
Open Access
Catching the moment with LoL + in twitch-like low-latency live streaming platforms
(IEEE, 2022) Bentaleb, A.; Akçay, Mehmet Necmettin; Lim, M.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz; Akçay, Mehmet Necmettin
Our earlier Low-on-Latency (dubbed as LoL) solution offered an accurate bandwidth prediction and rate adaptation algorithm tailored for live streaming applications that targeted an end-to-end latency of up to two seconds. While LoL was a significant step forward in multi-bitrate low-latency live streaming, further experimentation and testing showed that there was room for improvement in three areas. First, LoL used hard-coded parameters computed from an offline training process in the rate adaptation algorithm and this was seen as a significant barrier in LoL's wide deployment. Second, LoL's objective was to maximize a collective QoE function. Yet, certain use cases have specific objectives besides the singular QoE and this had to be accommodated. Third, the adaptive playback speed control failed to produce satisfying results in some scenarios. Our goal in this paper is to address these areas and make LoL sufficiently robust to deploy. We refer to the enhanced solution as LoL+ which has been integrated to the official dash.js player in v3.2.0.
Open Access
Common media client data (CMCD): Initial findings
(Association for Computing Machinery, Inc, 2021-07-16) Bentaleb, A.; Lim, M.; Akçay, Mehmet Necmettin; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz; Akçay, Mehmet Necmettin
In September 2020, the Consumer Technology Association (CTA) published the CTA-5004: Common Media Client Data (CMCD) specification. Using this specification, a media client can convey certain information to the content delivery network servers with object requests. This information is useful in log association/analysis, quality of service/experience monitoring and delivery enhancements. This paper is the first step toward investigating the feasibility of CMCD in addressing one of the most common problems in the streaming domain: efficient use of shared bandwidth by multiple clients. To that effect, we implemented CMCD functions on an HTTP server and built a proof-of-concept system with CMCD-Aware dash.js clients. We show that even a basic bandwidth allocation scheme enabled by CMCD reduces rebuffering rate and duration without noticeably sacrificing the video quality.
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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 Cengiz
Today'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.
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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 Cengiz
Past 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.
Open Access
Foreword
(Association for Computing Machinery, Inc, 2018-06-12) Beğen, Ali Cengiz; Timmerer, C.; Zimmermann, R.; Schierl, T.; Computer Science; BEĞEN, Ali Cengiz
N/A
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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 Cengiz
The 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.
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Game theory based bitrate adaptation for Dash.Js reference player
(IEEE, 2018-11-28) Bentaleb, A.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz
Most existing DASH adaptive bitrate (ABR) schemes are designed to behave in their own self-interest and do not perform consistently in all network environments. In this work, we provide a practical implementation, materials and demonstration of a game theoretical ABR scheme, termed GTA [1], in the dash.js reference player. The GTA approach optimizes the viewer experience across multiple players without requiring explicit communication, and maintains a high playback bitrate while reducing startup delay, and minimizing quality switches and stalls.
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Meta reinforcement learning for rate adaptation
(IEEE, 2023) Bentaleb, A.; Lim, M.; Akçay, Mehmet Necmettin; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz; Akçay, Mehmet Necmettin
Adaptive bitrate (ABR) schemes enable streaming clients to adapt to time-varying network/device conditions to achieve a stall-free viewing experience. Most ABR schemes use manually tuned heuristics or learning-based methods. Heuristics are easy to implement but do not always perform well, whereas learning-based methods generally perform well but are difficult to deploy on low-resource devices. To make the most out of both worlds, we develop Ahaggar, a learning-based scheme running on the server side that provides quality-aware bitrate guidance to streaming clients running their own heuristics. Ahaggar's novelty is the meta reinforcement learning approach taking network conditions, clients' statuses and device resolutions, and streamed content as input features to perform bitrate guidance. Ahaggar uses the new Common Media Client/Server Data (CMCD/SD) protocols to exchange the necessary metadata between the servers and clients. Experiments on an open-source system show that Ahaggar adapts to unseen conditions fast and outperforms its competitors in several viewer experience metrics.
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Performance analysis of ACTE: A bandwidth prediction method for low-latency chunked streaming
(Association for Computing Machinery, Inc, 2020-07) Bentaleb, A.; Timmerer, C.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz
HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency. This allows media segments to be delivered while still being packaged. However, conventional schemes often make widely inaccurate bandwidth measurements due to the presence of idle periods between the chunks and hence this is causing sub-optimal adaptation decisions. To address this issue, we earlier proposed ACTE (ABR for Chunked Transfer Encoding) [6], a bandwidth prediction scheme for low-latency chunked streaming. While ACTE was a significant step forward, in this study we focus on two still remaining open areas, namely, (i) quantifying the impact of encoding parameters, including chunk and segment durations, bitrate levels, minimum interval between IDR-frames and frame rate on ACTE, and (ii) exploring the impact of video content complexity on ACTE. We thoroughly investigate these questions and report on our findings. We also discuss some additional issues that arise in the context of pursuing very low latency HTTP video streaming.
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QoE-aware bandwidth broker for HTTP adaptive streaming flows in an SDN-enabled HFC network
(IEEE, 2018-06) Bentaleb, A.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz
This paper proposes a software defined networking based bandwidth broker solution for improving viewer experience for any type of content delivered to any type of consumer device using HTTP adaptive streaming (HAS) in a hybrid fiber coax network. This solution is designed to meet per-session and per-group quality-of-experience objectives, to avoid common HAS culprits such as video instability, unfair and unequal quality distribution and network resource underutilization, and to scale to a large number of concurrent HAS sessions without introducing too much overhead. The mathematical framework behind our solution solves a convex optimization problem, which relies on a concave network utility maximization function. Results confirm the effectiveness of the proposed solution over the state-of-the-art bitrate adaptation and bandwidth allocation schemes.
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Road to salvation: Streaming clients and content delivery networks working together
(IEEE, 2021-11-01) Beğen, Ali Cengiz; Bentaleb, A.; Silhavy, D.; Pham, S.; Zimmermann, R.; Law, W.; Computer Science; BEĞEN, Ali Cengiz
Streaming media has truly become one of the most popular applications on the Internet. Viewers are spoiled for choice, and content providers compete to provide the best viewer experience. Traditionally, it has been challenging for content providers to get deep insights into the performance of their large-scale streaming operations. A new standard, Common Media Client Data (CMCD), aims to fundamentally change this. After providing an overview of this standard and describing some possible application scenarios, we present an open source sample implementation for readers to explore this topic further in their own practical environments.
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SDNDASH: Improving QoE of HTTP Adaptive Streaming Using Software Defined Networking
(ACM, 2016) Bentaleb, A.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz
HTTP adaptive streaming (HAS) is being adopted with increasing frequency and becoming the de-facto standard for video streaming. However, the client-driven, on-off adaptation behavior of HAS results in uneven bandwidth competition and this is exacerbated when a large number of clients share the same bottleneck network link and compete for the available bandwidth. With HAS each client independently strives to maximize its individual share of the available bandwidth, which leads to bandwidth competition and a decrease in end-user quality of experience (QoE). The competition causes scalability issues, which are quality instability, unfair bandwidth sharing and network resource underutilization. We propose a new software defined networking (SDN) based dynamic resource allocation and management architecture for HAS systems, which aims to alleviate these scalability issues and improve the per-client QoE. Our architecture manages and allocates the network resources dynamically for each client based on its expected QoE. Experimental results show that the proposed architecture significantly enhances scalability by improving per-client QoE by at least 30% and supporting up to 80% more clients with the same QoE compared to the conventional schemes.
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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 Cengiz
HTTP 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.
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A survey on bitrate adaptation schemes for streaming media over HTTP
(IEEE, 2019) Bentaleb, A.; Taani, B.; Beğen, Ali Cengiz; Timmerer, C.; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz
In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive streaming (HAS). As a key distinction from other streaming approaches, the bitrate adaptation algorithms in HAS are chiefly executed at each client, i.e., in a distributed manner. The objective of these algorithms is to ensure a high quality of experience (QoE) for viewers in the presence of bandwidth fluctuations due to factors like signal strength, network congestion, network reconvergence events, etc. While such fluctuations are common in public Internet, they can also occur in home networksor even managed networks where there is often admission control and QoS tools. Bitrate adaptation algorithms may take factors like bandwidth estimations, playback buffer fullness, device features, viewer preferences, and content features into account, albeit with different weights. Since the viewer's QoE needs to be determined in real-time during playback, objective metrics are generally used including number of buffer stalls, duration of startup delay, frequency and amount of quality oscillations, and video instability. By design, the standards for HAS do not mandate any particular adaptation algorithm, leaving it to system builders to innovate and implement their own method. This survey provides an overview of the different methods proposed over the last several years.
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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 Cengiz
In 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%.
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When they go high, we go low: low-latency live streaming in dash.js with LoL
(The ACM Digital Library, 2020-05) Lim, M.; Akçay, Mehmet Necmettin; Bentaleb, A.; Beğen, Ali Cengiz; Zimmermann, R.; Computer Science; BEĞEN, Ali Cengiz; Akçay, Mehmet Necmettin
Live streaming remains a challenge in the adaptive streaming space due to the stringent requirements for not just quality and rebuffering, but also latency. Many solutions have been proposed to tackle streaming in general, but only few have looked into better catering to the more challenging low-latency live streaming scenarios. In this paper, we re-visit and extend several important components (collectively called Low-on-Latency, LoL) in adaptive streaming systems to enhance the low-latency performance. LoL includes bitrate adaptation (both heuristic and learning-based), playback control and throughput measurement modules.