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DDRC'21 2021 : IEEE International Workshop on Data-Driven Rate Control for Media Streaming

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Link: https://sites.google.com/view/bigmm2021-ddrc
 
When Nov 15, 2021 - Nov 17, 2021
Where Taichung, Taiwan
Submission Deadline Oct 6, 2021
Notification Due Oct 20, 2021
Final Version Due Oct 27, 2021
Categories    machine learning   streaming   multimedia   real time communication
 

Call For Papers

Co-located with the IEEE International Conference on Multimedia Big Data (BigMM’21)

Workshop Co-Charis:
Prof. Chung-Ying Huang, National Yang Ming Chiao Tung University, Taiwan
Dr. Chih-Fan Hsu, Inventec Corporation, Taiwan
Prof. Xin Liu, University of California, Davis, United States

Theme:
While the usage of streaming services has skyrocketed due to the Covid-19 pandemics, sustaining good user experience is still challenging because of the dynamics of network conditions, especially for extremely low-latency applications. The first Data-Driven Rate Control for Media Streaming (DDRC) workshop aims to present and discuss recent advances in data-driven rate control technologies, including but not limited to low-latency scenarios and real-time communication. It also advocates to explore and understand the research challenges in new approaches for controlling the rate according to user experience. Specifically, the workshop intends to address the following objectives:
1. Research challenges in developing new rate-control techniques for media streaming services;
2. New visions and concepts that will drive the evolution of rate control mechanisms to avoid video/audio impairment caused by dynamic network conditions; and
3. Deployment challenges that arise when applying new rate control mechanisms to mobile and desktop platforms.

With the workshop, we hope to foster interaction among researchers and exchange new ideas by bringing together content, systems, and networking communities with a specific focus on media streaming. The goal is to gather active researchers and practitioners in this important field to gain insight from their experiences and to inspire new approaches. Our ambition in this incarnation is to bring together a wider group of researchers involved in addressing data-driven rate control from different perspectives including data collection, mechanism designs, and technology deployment. We believe that a forum that allows experts in these communities to interact with each other will support a more holistic approach to future research in streaming. In addition, the workshop provides an exciting venue to discuss existing challenges, best practices, and new ideas among the academic and industrial communities in terms of introducing the data-driven rate control model to support streaming.

Topics:
The workshop will solicit original and unpublished research achievements in various aspects, including, but not limited to, the following topics:
Data-driven adaptive rate media solutions
Cross-layer architectures and technologies for rate control
Congestion control for media streaming
Quality of experience for media streaming
Performance study on streaming
QoE and QoS estimation and measurement
Design for subjective quality assessments
Media streaming systems over heterogeneous networks and devices
Realistic simulator based on real-world data

Submission of Manuscript:
Papers should be formatted in IEEE-style format (https://www.ieee.org/conferences/publishing/templates.html) and not longer than eight pages of text using 10 point size font on letter paper. The page limit includes tables, figures, and references. Papers will be peer-reviewed and selected based on their originality, technical merit, and topical relevance. Authors should submit a PDF file at the submission site: https://optimus.cs.nthu.edu.tw/bigmm_streaming/, following the submission instructions on the workshop website.

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