Abdelhak Bentaleb

Abdelhak Bentaleb

National University of Singapore

H-index: 17

Asia-Singapore

Description

Abdelhak Bentaleb, With an exceptional h-index of 17 and a recent h-index of 17 (since 2020), a distinguished researcher at National University of Singapore, specializes in the field of Networking, Multimedia Systems and Communication, Video Delivery and Edge AI, Game Systems and Streaming, Applied Machine and Re.

Professor Information

University

National University of Singapore

Position

Postdoctoral Research Fellow at

Citations(all)

1561

Citations(since 2020)

1360

Cited By

611

hIndex(all)

17

hIndex(since 2020)

17

i10Index(all)

25

i10Index(since 2020)

24

Email

University Profile Page

National University of Singapore

Research & Interests List

Networking

Multimedia Systems and Communication

Video Delivery and Edge AI

Game Systems and Streaming

Applied Machine and Re

Top articles of Abdelhak Bentaleb

ARTEMIS: Adaptive bitrate ladder optimization for live video streaming

Live streaming of segmented videos over the Hypertext Transfer Protocol (HTTP) is increasingly popular and serves heterogeneous clients by offering each segment in multiple representations. A bitrate ladder expresses this choice as a list of bitrate-resolution pairs. Whereas existing solutions for HTTP-based live streaming use a static bitrate ladder, the fixed ladders struggle to appropriately accommodate the dynamics in the video content and network-conditioned client capabilities. This paper proposes ARTEMIS as a practical scalable alternative that dynamically configures the bitrate ladder depending on the content complexity, network conditions, and clients' statistics. ARTEMIS seamlessly integrates with the end-to-end streaming pipeline and operates transparently to video encoders and clients. We develop a cloud-based implementation of ARTEMIS and conduct extensive real-world and trace-driven experiments. The experimental comparison vs. existing prominent bitrate ladders demonstrates that live streaming with ARTEMIS outperforms all baseline solutions, reduces encoding computation by 25%, end-to-end latency by 18%, and increases the quality of experience by 11%.

Authors

Farzad Tashtarian,Abdelhak Bentaleb,Hadi Amirpour,Sergey Gorinsky,Junchen Jiang,Hermann Hellwagner,Christian Timmerer

Published Date

2024/4/16

Bitrate Adaptation and Guidance with Meta Reinforcement Learning

Adaptive bitrate (ABR) schemes enable streaming clients to adapt to time-varying network/device conditions for 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 earlier developed Ahaggar , a learning-based scheme executing 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 …

Authors

Abdelhak Bentaleb,May Lim,Mehmet N Akcay,Ali C Begen,Roger Zimmermann

Journal

IEEE Transactions on Mobile Computing

Published Date

2024/3/12

Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P. 1203

With end-to-end encryption for video streaming services becoming more popular, network administrators face new challenges in preserving network performance and user experience. Video ads may cause traffic congestion and poor Quality of Experience. Because of the natural variation in user interests and network situations, traditional algorithms for increasing QoE may face limitations. To solve this problem, we suggest a novel method that uses user facial emotion recognition to deduce QoE and study the effect of ads. We use open-access Face Emotion Recognition (FER) datasets and extract facial emotion information from actual observers to train machine learning models. Participants were requested to watch ad videos and provide feedback, which will be used for comparison, training, testing, and validation of our suggested technique. Our tests show that our approach beats the ITU-T P. 1203 standard in terms of accuracy by 37.1%. Our method provides a hopeful answer to the problem of increasing user engagement and experience in video streaming services.

Authors

Tisa Selma,Abdelhak Bentaleb,Mohammad Mehedy Masud,Saad Harous

Published Date

2024/2/2

CP-Steering: CDN-and Protocol-Aware Content Steering Solution for HTTP Adaptive Video Streaming

In recent years, HTTP Adaptive Streaming (HAS)-based technologies, such as Dynamic Adaptive Streaming over HTTP (DASH), have become the predominant video delivery paradigm over the Internet. HAS-based content providers frequently employ multiple Content Delivery Network (CDNs) to distribute their content to the end users. Recently, Apple and DASH-IF standardization introduced content steering technique to enable content providers to switch the content source that a player utilizes at start-up or midstream. Due to diverse adaptive video streaming demands for latency-sensitive and/or bandwidth-sensitive streams, satisfying end users with a pleasant Quality of Experience (QoE) poses a new challenge for redesigning the current content steering strategies. This paper leverages the recent popular Quick UDP Internet Connections (QUIC) transport protocol and introduces a CDN- and Protocol-aware …

Authors

Reza Farahani,Abdelhak Bentaleb,Mohammad Shojafar,Hermann Hellwagner

Published Date

2023/5/7

A real-time blind quality-of-experience assessment metric for http adaptive streaming

In today’s Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm. An effective Quality of Experience (QoE) assessment metric can provide crucial feedback to an ABR algorithm. However, predicting such real-time QoE on the client side is challenging. The QoE prediction requires high consistency with the Human Visual System (HVS), low latency, and blind assessment, which are difficult to realize together. To address this challenge, we analyzed various characteristics of HAS systems and propose a non-uniform sampling metric to reduce time complexity. Furthermore, we design an effective QoE metric that integrates resolution and rebuffering time as the Quality of Service (QoS), as well as spatiotemporal output from a deep neural network and specific switching events as content …

Authors

Chunyi Li,May Lim,Abdelhak Bentaleb,Roger Zimmermann

Published Date

2023/7/10

Bandwidth prediction in low-latency media transport

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.

Authors

Abdelhak Bentaleb,Mehmet N Akcay,May Lim,Ali C Begen,Roger Zimmermann

Published Date

2023/5/7

SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications

5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multime-dia Services (MS). To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs …

Authors

Reza Farahani,Abdelhak Bentaleb,Christian Timmerer,Mohammad Shojafar,Radu Prodan,Hermann Hellwagner

Published Date

2023/5/28

Chunk-based prediction adaptation logic

A multimedia player downloads chunks (parts of the segment file) during the download of a segment of a stream of segments of a low-latency stream. The first chunks of a segment may be downloaded while the segment is still being written to the CDN server. A chunk-based prediction adaptation logic uses throughput measurements on a chunk instead of a segment and specifically looks at bursts in a sliding window. This data is used to build a prediction of future throughput by applying linear adaptive filter algorithms that may rely on recursive least squares. This adaptation logic leads to very accurate bandwidth predictions and as a consequence, better user experience, compared to existing adaptation algorithms.

Published Date

2023/4/4

Professor FAQs

What is Abdelhak Bentaleb's h-index at National University of Singapore?

The h-index of Abdelhak Bentaleb has been 17 since 2020 and 17 in total.

What are Abdelhak Bentaleb's research interests?

The research interests of Abdelhak Bentaleb are: Networking, Multimedia Systems and Communication, Video Delivery and Edge AI, Game Systems and Streaming, Applied Machine and Re

What is Abdelhak Bentaleb's total number of citations?

Abdelhak Bentaleb has 1,561 citations in total.

What are the co-authors of Abdelhak Bentaleb?

The co-authors of Abdelhak Bentaleb are Roger Zimmermann, Christian Timmerer, Yong Cui, Thomas Zinner, Wei Tsang Ooi, Saad Harous.

Co-Authors

H-index: 53
Roger Zimmermann

Roger Zimmermann

National University of Singapore

H-index: 44
Christian Timmerer

Christian Timmerer

Alpen-Adria-Universität Klagenfurt

H-index: 42
Yong Cui

Yong Cui

Tsinghua University

H-index: 30
Thomas Zinner

Thomas Zinner

Norges teknisk-naturvitenskaplige universitet

H-index: 30
Wei Tsang Ooi

Wei Tsang Ooi

National University of Singapore

H-index: 25
Saad Harous

Saad Harous

United Arab Emirates University

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