IEEE JSAC: Multimedia Economics for Future Networks: Theory, Methods, and Applications

Guest Editorial

Multimedia Economics for Future Networks: Theory, Methods, and Applications

Authors: Wen Ji, Zhu Li, H. Vincent Poor, Christian Timmerer, and Wenwu Zhu

Abstract: With the growing integration of telecommunication networks, Internet of Things (IoT), and 5G networks, there is a tremendous demand for multimedia services over heterogeneous networks. According to recent survey reports, mobile video traffic accounted for 60 percent of total mobile data traffic in 2016, and it will reach up to 78 percent by the end of 2021. Users’ daily lives are inundated with multimedia services, such as online video streaming (e.g., YouTube and Netflix), social networks (e.g., Facebook, Instagram, and Twitter), IoT and machine generated video (e.g, surveillance cameras), and multimedia service providers (e.g., Over-the-Top (OTT) services). Multimedia data is thus becoming the dominant traffic in the near future for both wired and wireless networks.

W. Ji, Z. Li, H. V. Poor, C. Timmerer and W. Zhu, "Guest Editorial Multimedia Economics for Future Networks: Theory, Methods, and Applications," in IEEE Journal on Selected Areas in Communications, vol. 37, no. 7, pp. 1473-1477, July 2019.
doi: 10.1109/JSAC.2019.2918962

Link: https://doi.org/10.1109/JSAC.2019.2918962

DASH-IF awarded Best PhD Dissertation on Algorithms and Protocols for Adaptive Content Delivery over the Internet

For the first time, the DASH Industry Forum (DASH-IF) awarded the best PhD dissertation on algorithms and protocols for adaptive content delivery over the internet at ACM MMSys 2019. After a public call, the DASH-IF received a good number of nominations from all around the world to pick one winner. Due to the high number and quality of nominations (and since it's the first time), the DASH-IF decided to award also the second and third best dissertation; increasing its financial prize as follows: first place – $750; second place – $500; and third place – $250. The winners are chosen by a DASH Industry Forum appointed committee and results are final.

And the winner is...

First place

Dr. Abdelhak Bentaleb (National University of Singapore)
for the dissertation

Enabling Optimizations of Video Delivery in HTTP Adaptive Streaming

(From left to right): R. Zimmermann (supervisor), A. Bentaleb, A. Giladi (DASH-IF); (c) Ishita Dasgupta

Second place

Dr. Jeroen van der Hooft (Ghent University), Low-Latency Delivery of Adaptive Video Streaming Services

(From left to right): Jeroen van der Hooft, A. Giladi (DASH-IF); (c) Ishita Dasgupt

Third place

Dr. Jonathan Kua (Swinburne University of Technology), Achieving High Performance Content Streaming with Adaptive Chunklets and Active Queue Management

The DASH-IF would like to congratulate all winners and hope seeing you next year at ACM MMSys 2020.

DASH-IF awarded Excellence in DASH award at ACM MMSys 2019

The DASH Industry Forum Excellence in DASH Award at ACM MMSys 2019 acknowledges papers substantially addressing MPEG-DASH as the presentation format and are selected for presentation at ACM MMSys 2019. Preference is given to practical enhancements and developments which can sustain future commercial usefulness of DASH. The DASH format used should conform to the DASH-IF Interoperability Points as defined by http://dashif.org/guidelines/. It is a financial prize as follows: first place – €1000; second place – €500; and third place – €250. The winners are chosen by a DASH Industry Forum appointed committee and results are final.

This year's award goes to the following papers (Two first places, and one third):

1. Stefan Pham, Patrick Heeren, Daniel Silhavy, Stefan Arbanowski, Evaluation of shared resource allocation using SAND for ABR streaming

(From left to right): S. Pham, A. Giladi (DASH-IF); (c) Ishita Dasgupta

1. Abdelhak Bentaleb, Christian Timmerer, Ali C. Begen, Roger Zimmermann, Bandwidth prediction in low-latency chunked streaming

(From left to right): A. Bentaleb, A. Giladi (DASH-IF); (c) Ishita Dasgupta

3. Xing Liu, Bo Han, Feng Qian, Matteo Varvello, LIME: understanding commercial 360° live video streaming services

(From left to right): X. Liu, A. Giladi (DASH-IF); (c) Ishita Dasgupta

The DASH-IF would like to congratulate all winners and hope seeing you next year at ACM MMSys 2020.

Video Developer Survey 2019: Call for Participation

After the success of 2018 and 2017 editions of the Video Developer Survey, Bitmovin is calling once again for your participation in this years survey, which is accessible here:

https://www.surveymonkey.com/r/2019developersurvey_int

The goal is to learn from you about the video codecs, formats and platforms used in your organization and how you see the tech evolving in the next year. 

An example outcome from last years edition is shown below, which illustrates the planned video codec usage in the next 12 months compared to the 2017 report.

Planned video codec usage in the next 12 months 2017 vs. 2018.

QUALINET Online Lecture: On The Privacy Preserving Modelling for QoE

Presenters: Selim Ickin and Jörgen Gustafsson, Ericsson Research

When: 21 June, 2019 - 11:00-12:00 CEST

Where: Adobe Connect (https://collab.switch.ch/qualinet-training)

Abstract:
Machine Learning models in the area of QoE potentially suffer from over-fitting due to limitations including low data volume, and participant profile. This might prevent models being generic if the QoE ML problem is not well formulated, hence these trained models might have risk of performing unexpectedly when tested outside the experimented population. One reason for the limited datasets, which is referred as QoE data lakes, is due to the fact that often these datasets potentially contain user sensitive information, and are only collected throughout expensive user studies with special user consent. Thus, sharing of datasets amongst researchers has been challenging. In this talk, we will discuss on a few state of the art privacy preserving machine learning training techniques that potentially enables sharing of learned knowledge amongst different small data lakes.