IEEE ICME 2016 Grand Challenge: Dynamic Adaptive Streaming over HTTP

ICME 2016: http://www.icme2016.org/

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HOT NEWS

  • Winner: A BIO-INSPIRED HTTP-BASED ADAPTIVE STREAMING PLAYER
    • Yusuf Sani (Lancaster University)
    • Mu Mu (The University of Northampton)
    • Andreas Mauthe (Lancaster University)
    • Christopher Edwards (Lancaster University)
meeting-CT

A. C. Gallagher, Y. Sani, C. Timmerer (from left to right)

  • Accepted grand challenge submissions: A BIO-INSPIRED HTTP-BASED ADAPTIVE STREAMING PLAYER, AN ADAPTATIVE BITRATE ALGORITHM FOR DASH, Buffer-based Control Theoretic Approach for Dynamically HTTP Streaming (see details below)
  • Bitmovin IEEE ICME’16 Grand Challenge presentation slot: Tuesday, July 12, 2016, 17:00-1800, Grand III (each presentation ~20min incl. Q&A)

Challenge Description

Real-time entertainment services such as streaming video and audio are currently accounting for more than 60% of the Internet traffic during peak hours. Interestingly, these services are all delivery over-the-top (OTT) of the existing networking infrastructure using the Hypertext Transfer Protocol (HTTP) which resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (DASH). The MPEG-DASH standard enables smooth multimedia streaming towards heterogeneous devices and commonly assumes the usage of HTTP-URLs to identify the segments available for the clients.

The MPEG-DASH standard provides an interoperable representation format but deliberately does not define the adaptation behaviour within the client implementations that is left open for research and industry competition. In a typical deployment, the encoding itself is optimised for the respective delivery channels but – as the content is delivery over the top of existing networks without any guarantees – various issues during the streaming (e.g., low startup delay, stalls/re-buffering, high switching frequency, inefficient network utilisation, competing network traffic, network infrastructure costs, etc. etc. etc.) may limit the Quality of Experience (QoE) as perceived by the end user.

The aim of this grand challenge is to solicit contributions addressing end-to-end delivery aspects which improve the QoE while optimally utilising the available network infrastructures and its associated costs. This includes the content preparation for DASH, the content delivery within existing networks, and the client implementations.

Dataset/APIs/Library URL

Evaluation Criteria

1. Evaluation of Streaming Performance

Each proposal shall be evaluated according to a predefined and well-established evaluation setup as documented in the current literature. This could be done using network emulation (based on synthetic or real-world network traces) or in real-world environments. The evaluation can be done within the lab and/or crowdsourcing using objective and subjective metrics.

The streaming performance shall be evaluated using the following metrics: initial/start-up delay, buffer underruns also known as stalls, quality switches, media throughput, network utilisation, network costs, and objective (e.g., PSNR, SSIM, …) and subjective (MOS) quality scores.

2. Evaluation Methodology

Proposals for new evaluation methodologies to evaluate the streaming performance should contain a detailed description including dataset and source code of the methodology. This could include any new objective and subjective evaluation methods in the broad area of adaptive media streaming over HTTP.

3. Disruptive Technology

Proposals for new, disruptive ways of video streaming are welcome along with proposals for content preparation, streaming, consumption, and evaluation methodologies. Such content should be made freely available for research and standardization purposes.

Deadline of Submission: April 3, 2016

Submission Guidelines

Submissions of DASH implementations or evaluation methodologies should provide a detailed technical description in the form of a short paper as well as material to validate the performance of the submission (e.g., dataset and binary executable to reconstruct and measure performance). Submissions for additional content should be made along with either a streaming algorithm or an evaluation methodology. The paper will be included as part of the ICME proceedings and published on IEEE Xplore if accepted after peer review.

Submission sitehttps://cmt.research.microsoft.com/ICMEW2016 (Track: Grand Challenges, Subject Areas: bitmovin Grand Challenge: Dynamic Adaptive Streaming over HTTP) For detailed submission instructions, see http://www.icme2016.org/ under “Authors -> Autors Information and Submission Instructions, then scroll down to Grand Challenges” (and see below)

Additional Information

A winner in each category will be selected by a judging committee. A financial price for the winner is envisaged including a bitmovin Goodie Bag (surprise, surprise)!

Questions and requests

If you have any questions or requests, or need further clarifications, please contact the organiser.

Detailed Submission Guidelines

If you would like to propose a Grand Challenge, please see Call for Grand Challenges for more information. Grand Challenge proposals should be submitted by November 30, 2015.

If you are submitting a contribution to an approved Grand Challenge, please see the appropriate Grand Challenge site for specific information. Most Grand Challenges will have the following commonalities:

  • Submission deadline: April 3, 2016.
  • Format: Challenges may solicit a written component and/or a data component. For challenges that solicit a written component, the corresponding papers will generally be formatted like an Industry Track paper, with a 4-page limit. For challenges that solicit data for evaluation, the data format will be specified on the appropriate Grand Challenge site.
  • Submission: Submit the written component via https://cmt.research.microsoft.com/ICMEW2016 under the appropriate Grand Challenge track. Submit the data component, if any, directly to the Grand Challenge organizers as specified on the appropriate Grand Challenge site.
  • Review: Submissions of both written and data components will be reviewed directly by the Grand Challenge organizers. Accepted submissions (written component only) will be included in the USB Proceedings and the authors will be given the opportunity to present their work at ICME. “Winning” submissions will be announced by the Grand Challenge organizers at the conference. Accepted submissions may or may not be eligible for publication on IEEE Xplore depending on the Grand Challenge; details will be available later.
  • Notification of acceptance: April 22, 2016.
  • Camera-ready deadline: May 13, 2016.

Accepted Grand Challenge Submissions

Submission ID: 190

Title: A BIO-INSPIRED HTTP-BASED ADAPTIVE STREAMING PLAYER

Abstract: In order to streamline video content distribution on a myriad of platforms over heterogeneous networks, HTTP Adaptive Streaming (HAS) has been increasingly adopted. In this paper we pilot a bio-inspired HAS optimisation design with the aim of maximising the overall user experience of a video playback session. Evaluations conducted within a real-world Internet environment, using quality indicators such as convergence time, start-up delay, average video rate, stability, and fairness, demonstrate the benefits of our design.

Authors:

  • Yusuf Sani (Lancaster University)
  • Mu Mu (The University of Northampton)
  • Andreas Mauthe (Lancaster University)
  • Christopher Edwards (Lancaster University)

Submission ID: 191

Title: AN ADAPTATIVE BITRATE ALGORITHM FOR DASH

Abstract: Dynamic adaptive streaming over HTTP (DASH) has been widely used on the Internet. However, DASH does not
impose any algorithm to choose video quality. In this paper, an adaptive bitrate switch algorithm for DASH player is proposed. Firstly, the proposed algorithm takes video playback quality, video rate switching frequency and buffer status into account in order to meet the available bandwidth. Secondly, several measures are designed specially to improve user visual quality. Besides, the proposed player takes a sub-optimal algorithm to avoid video playback interruptions. Experimental results demonstrate that the proposed player can provide better performance compared with Bitdash player in several aspects, such as video quality switch frequency, bandwidth utilization and subjective visual experience.

Authors:

  • Yunlong Li (Peking University)
  • Yue Wang (Peking University)
  • Shanshe Wang (Peking University)
  • Siwei Ma (Peking University)

Submission ID: 194

Title: Buffer-based Control Theoretic Approach for Dynamically HTTP Streaming

Track: Grand Challenges

Abstract: Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet. It, however, does not impose any adaptation logic for selecting the quality of video fragments requested by clients. In this challenge, we have designed a novel rate adaptation scheme for Dynamic HTTP Streaming, by which, low start-up time, continuous and smooth video playback, and high bandwidth utilization are obtained. The algorithm is mainly based on our previous work that a PD controller are adopted to guide the rate adaptation. Moreover, we further improve the performance of start-up delay by fast-start approach and dynamic buffer size adjustment. The numerous experiment results have demonstrated the good performance of our designed rate adaptation compared with the Bitdash.

Authors:

  • Zhimin Xu (Peking University)
  • Chao Zhou (Peking University)
  • Li Liu (Peking University)
  • XINGGONG ZHANG (Peking University)
  • Zongming Guo (Peking University)
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