Browsing by Author "Lim, M."

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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.
Metadata only
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.
Metadata only
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.