ATHENA Papers Accepted at ACM MMSys’21 Doctoral Symposium
ACM Multimedia Systems Conference (MMSys) 2021 | Doctoral Symposium
September 28 – October 01, 2021 | Istanbul, Turkey
Multimedia Communication
Paper published at Frontiers in Robotics and AI: Information Distribution in Multi-Robot Systems: Generic, Utility-Aware Optimization Middleware
Authors: M. Barciś, A. Barciś, N. Tsiogkas, H. Hellwagner.
Title: Information Distribution in Multi-Robot Systems: Generic, Utility-Aware Optimization Middleware.
Frontiers in Robotics and AI 8:685105, July 2021.
This work addresses the problem of what information is worth sending in a multi-robot system under generic constraints, e.g., limited throughput or energy. Our decision method is based on Monte Carlo Tree Search. It is designed as a transparent middleware that can be integrated into existing systems to optimize communication among robots. Furthermore, we introduce techniques to reduce the decision space of this problem to further improve the performance. We evaluate our approach using a simulation study and demonstrate its feasibility in a real-world environment by realizing a proof of concept in ROS 2 on mobile robots.
Paper accepted at IEEE ACCESS: LwTE: Light-weight Transcoding at the Edge
Authors: Alireza Erfanian* (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hadi Amirpour*, (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)
*These authors contributed equally to this work.
Link: IEEE Access
Abstract: Due to the growing demand for video streaming services, providers have to deal with increasing resourcerequirements for increasingly heterogeneous environments. To mitigate this problem, many works have beenproposed which aim to (i) improve cloud/edge caching efficiency, (ii) use computation power available in thecloud/edge for on-the-fly transcoding, and (iii) optimize the trade-off among various cost parameters,e.g.,storage, computation, and bandwidth. In this paper, we proposeLwTE, a novelLight-weightTranscodingapproach at theEdge, in the context of HTTP Adaptive Streaming (HAS). During the encoding processof a video segment at the origin side, computationally intense search processes are going on. The mainidea ofLwTEis to store the optimal results of these search processes as metadata for each video bitrateand reuse them at the edge servers to reduce the required time and computational resources for on-the-fly transcoding.LwTEenables us to store only the highest bitrate plus corresponding metadata (of verysmall size) for unpopular video segments/bitrates. In this way, in addition to the significant reduction inbandwidth and storage consumption, the required time for on-the-fly transcoding of a requested segment isremarkably decreased by utilizing its corresponding metadata; unnecessary search processes are avoided.Popular video segments/bitrates are being stored. We investigate our approach for Video-on-Demand (VoD)streaming services by optimizing storage and computation (transcoding) costs at the edge servers and thencompare it to conventional methods (store all bitrates, partial transcoding). The results indicate that ourapproach reduces the transcoding time by at least 80% and decreases the aforementioned costs by 12% to70% compared to the state-of-the-art approaches.
Keywords: Video streaming, transcoding, video on demand, edge computing.
Paper accepted at IEEE 23rd International Workshop on Multimedia Signal Processing – WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices
Title: WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices
IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)
October 06-08, Tampere, Finland
Authors: Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), and Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)
Abstract: Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.
Keywords: ABR Algorithms, HTTP Adaptive Streaming, ITU-T P.1203, WISH
Accepted Paper at IEEE 23rd International Workshop on Multimedia Signal Processing – INCEPT: INTRA CU Depth Prediction for HEVC
Title: INCEPT: INTRA CU Depth Prediction for HEVC
IEEE 23rd International Workshop on Multimedia Signal Processing
October 06–08, 2021, Tampere, Finland
Authors: Vignesh V Menon (Alpen-Adria-Universitat Klagenfurt); Hadi Amirpour (Alpen-Adria-Universität Klagenfurt); Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria); Mohammad Ghanbari (University of Essex, UK).
Abstract: High Efficiency Video Coding (HEVC) improves the encoding efficiency by utilizing sophisticated tools such as flexible Coding Tree Unit (CTU) partitioning. The Coding Unit (CU) can be split recursively into four equally sized CUs ranging from 64×64 to 8×8 pixels. At each depth level (or CU size), intra prediction via exhaustive mode search was exploited in HEVC to improve the encoding efficiency and result in a very high encoding time complexity. This paper proposes an Intra CU Depth Prediction (INCEPT) algorithm, which limits Rate-Distortion Optimization (RDO) for each CTU in HEVC by utilizing the spatial correlation with the neighboring CTUs, which is computed using a DCT energy-based feature. Thus, INCEPT reduces the number of candidate CU sizes required to be considered for each CTU in HEVC intra coding. Experimental results show that the INCEPT algorithm achieves a better trade-off between the encoding efficiency and encoding time saving (i.e., BDR/∆T) than the benchmark algorithms. While BDR/∆T is 12.35% and 9.03% for the benchmark algorithms, it is 5.49% for the proposed algorithm. As a result, INCEPT achieves a 23.34% reduction in encoding time on average while incurring only a 1.67% increase in bit rate than the original coding in the x265 HEVC open-source encoder.
Keywords: HEVC, Intra coding, CTU, CU, depth decision
Farzad Tashtarian and Christian Timmerer Co-Chairs of ViSNext 2021 Workshop at the ACM CoNEXT 2021 Conference
ViSNext’21: 1st ACM CoNEXT Workshop on Design, Deployment, and Evaluation of Network-assisted Video Streaming
In recent years, we have witnessed phenomenal growth in live video traffic over the Internet, accelerated by the rise of novel video streaming technologies, advancements in networking paradigms, and our ability to generate, process, and display videos on heterogeneous devices. Regarding the existing constraints and limitations in different components on the video delivery path from the origin server to clients, the network plays an essential role in boosting the perceived Quality of Experience (QoE) by clients. The ViSNext workshop aims to bring together researchers and developers working on all aspects of video streaming, in particular network-assisted concepts backed up by experimental evidence. Read more about the workshop, call for papers at ViSNext2021 and registration here.
Paper accepted: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
Conference info: IEEE LCN
Authors: Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)
Abstract: Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.
Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE).
Throwback on our special session at PCS’21
A Special Session on ‘Video Coding for Large Scale HTTP Adaptive Streaming Deployments‘ was organized by Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Ghanbari (University of Essex, UK), and Alex Giladi (Comcast, USA) on July 2 at the 35th Picture Coding Symposium (PCS) 2021. Read more about it here.
Paper accepted: A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP
Conference info: The 46th IEEE Conference on Local Computer Networks (LCN) October 4-7, 2021
Authors: Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Abdelhak Bentaleb (National University of Singapore), Reza Farahani (Alpen-Adria-Universität Klagenfurt), Minh Nguyen (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt), and Roger Zimmermann (National University of Singapore)
Abstract: Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGCsystems are forces to made difficult trade-offs with video quality and latency. Read more
Paper accepted – CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
Conference info: The 46th IEEE Conference on Local Computer Networks (LCN) October 4-7, 2021
Authors: Reza Farahani (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)
Abstract: With the increasing demand for video streaming applications, HTTP Adaptive Streaming (HAS) technology has become the dominant video delivery technique over the Internet. Current HAS solutions only consider either client- or server-side optimization, which causes many problems in achieving high-quality video, leading to sub-optimal users’ experience and network resource utilization. Recent studies have revealed that network-assisted HAS techniques, by providing a comprehensive view of the network, can lead to more significant gains in HAS system performance. Read more