Thursday, January 20, 2022

Days of Future Past: An Optimization-based Adaptive Bitrate Algorithm over HTTP/3

Days of Future Past: An Optimization-based Adaptive Bitrate Algorithm over HTTP/3

The ACM CoNEXT 2021 Workshop on the Evolution, Performance, and Interoperability of QUIC (EPIQ)

07 December 2021  | Munich, Germany (Online)

Workshop Website

[PDF][Slides][Video]

Daniele Lorenzi, Minh Nguyen, Farzad Tashtarian, Simone Milani, Herman Hellwagner, and Christian Timmerer
Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt


Abstract:
HTTP Adaptive Streaming(HAS) has become a predominant technique for delivering videos in the Internet. Due to its adaptive behaviour according to changing network conditions it may result in video quality variations that negatively impacts the Quality of Experience (QoE) of the user. In this paper, we propose Days of Future Past, an optimization-based Adaptive Bitrate (ABR) algorithm over HTTP/3. Days of Future Past takes advantage of an optimization model and HTTP/3 features, including (i) stream multiplexing, and (ii) request cancellation. We design a Mixed Integer Linear Programming (MILP) model that determines the optimal video qualities of both next segment requests and the segments currently located in the buffer. If better qualities for buffered segments are found, the client will send corresponding HTTP GET requests to retrieve them. Multiple segments (i.e., re-transmitted segments) might be downloaded simultaneously to upgrade some buffered but not yet played segments to avoid quality decreases using the stream multiplexing feature of QUIC. HTTP/3’s request cancellation will be used in case retransmitted segments will arrive at the client after their playout time. The experimental results shows that our proposed method is able to improve the QoE by up to 33.9 %.

Keywords: HTTP/3, QUIC, Days of Future Past, HAS, QoE

Wednesday, January 19, 2022

LwTE-Live: Light-weight Transcoding at the Edge for Live Streaming

LwTE-Live: Light-weight Transcoding at the Edge for Live Streaming 

The 1st ACM CoNEXT Workshop on Design, Deployment, 
and Evaluation of Network-assisted  video Streaming (ViSNext 2021)

Conference Website

[PDF][Slides][Video]

Alireza Erfanian, Hadi Amirpour, Farzad Tashtarian, Christian Timmerer, and Hermann Hellwagner
Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt


Abstract:
Live video streaming is widely embraced in video services, and its applications have attracted much attention in recent years. The increased number of users demanding high quality (e.g., 4K resolution) live videos increase the bandwidth utilization in the backhaul network. To decrease bandwidth utilization in HTTP Adaptive Streaming (HAS), in on-the-fly transcoding approaches, only the highest bitrate representation is delivered to the edge, and other representations are generated by transcoding at the edge. However, this approach is inefficient due to the high transcoding cost. In this paper, we propose a light-weight transcoding at the edge method for live applications, LwTE-Live, to decrease the band-width utilization and the overall live streaming cost. During the encoding processes at the origin server, the optimal encoding decisions are saved as metadata, and the metadata replaces the corresponding representation in the bitrate ladder. The significantly reduced size of the metadata compared to its corresponding representation decreases the bandwidth utilization. The extracted metadata is then utilized at the edge to decrease the transcoding time. We formulate the problem as a Mixed-Binary Linear Programming (MBLP) model to optimize the live streaming cost, including the bandwidth and computation costs. We compare the proposed model with state-of-the-art approaches and the experimental results show that our proposed method saves the cost and backhaul bandwidth utilization up to 34% and 45%, respectively.

Keywords: live video streaming, network function virtualization, NFV, light-weight transcoding, transcoding, edge computing

Tuesday, January 18, 2022

WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices

WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices

IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)

Conference Website

[PDF][Slides][Video]

October 06-08 | Tampere, Finland

Minh Nguyen, Ekrem Çetinkaya,  Hermann Hellwagner, 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

Sunday, January 16, 2022

CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming

CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming

The 46th IEEE Conference on Local Computer Networks (LCN)

Conference Website

[PDF][Slides][Video]

Reza Farahani, Farzad Tashtarian, Hadi Amirpour, Christian Timmerer, Mohammad Ghanbari, and Hermann Hellwagner
Christian Doppler Laboratory ATHENA, 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. In this paper, we leverage the capability of Software-Define Networking (SDN), Network Function Virtualization (NFV), and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming framework called CSDN. We employ virtualized edge entities to collect various information items (e.g., user-, client, CDN- and network-level information) in a time-slotted method. These components then run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers (in terms of fetch and transcoding times). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, (ii) by a quality transcoded from an optimal replacement quality at the edge, or (iii) by the originally requested quality level from the origin server. By means of comprehensive experiments conducted on a real-world large-scale testbed, we demonstrate that CSDN outperforms the state-of-the-art in terms of playback bitrate, the number of quality switches, the number of stalls, and bandwidth usage by at least 7.5%, 19%, 19%, and 63%, respectively.

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE), Software Defined Networking (SDN), Network Function Virtualization (NFV), Video Transcoding, Content Delivery Network (CDN).


Saturday, January 15, 2022

A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP

A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP

The 46th IEEE Conference on Local Computer Networks (LCN)

Conference Website

[PDF][Slides][Video]

Farzad Tashtarian*, Abdelhak Bentaleb**, Reza Farahani*, Minh Nguyen*, Christian Timmerer*, Hermann Hellwagner*, and Roger Zimmermann**
Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt
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, UGC systems are forced to make difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA (joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers (ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an optimal solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance of QuaLA in achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutions

Keywords: UGC streaming, low latency live streaming, fair-ness, QoE, HAS, DASH, ABR, adaptive streaming, ADMM.

Wednesday, January 12, 2022

EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

The 46th IEEE Conference on Local Computer Networks (LCN)

Conference Website

[PDF][Slides][Video]

Jesús Aguilar Armijo, Christian Timmerer and Hermann Hellwagner
Christian Doppler Laboratory ATHENA, 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).

Monday, January 10, 2022

QoMEX’22: 14th International Conference on Quality of Multimedia Experience

Call For Papers

QoMEX’22

14th International Conference on Quality of Multimedia Experience

September 5.-7. 2022 – Lippstadt, Germany

Full Paper Submission: March 31, 2022

https://qomex2022.itec.aau.at/


The 14th International Conference on Quality of Multimedia Experience will be held from September 5th to 7th, 2022 in Lippstadt, Germany. It will bring together leading experts from academia and industry to present and discuss current and future research on multimedia quality, quality of experience (QoE) and user experience (UX). This way, it will contribute to excellence in developing multimedia technology, towards user well-being, and it will foster the exchange between multidisciplinary communities.

The QoMEX 2022 team solicits contributions including but not limited to topics:

  • Immersive experiences and technologies
  • QoE, Big Data and Artificial Intelligence
  • Games User Research and Experience
  • New assessment and evaluation methods
  • Quality, experience, and user state
  • Quality of Life and Well-being
  • Multimodal perception & quality
  • Databases for QoE research
  • Audio/Visual user experience
  • QoE-aware networks and services management

Prospective authors are invited to submit full (maximum of 6 pages) or short papers (3 +1 page of references) to the general track and to special sessions. Each paper will undergo a double-blind review process. Full and short papers will be included in the conference proceedings and published in IEEExplore (approval pending).

Important Dates and Details

  • Full Paper Submission: 31 March 2022
  • Full Paper Notification: 20 May 2022
  • Short Paper Submission: 31 May 2022
  • Short Paper Notification: 06 July 2022
  • Conference: September 5-7, 2022

Website: https://qomex2022.itec.aau.at

Twitter: @QoMEXconf

 

General Chair

  • Jan-Niklas Voigt-Antons, HSHL, Germany

Technical Program Chairs

  • Luigi Atzori, Univ. Cagliari,  Italy
  • Sebastian Möller, TU Berlin/DFKI Berlin, Germany
  • Alexander Raake, TU Ilmenau, Germany