ACM Multimedia Systems Conference (MMSys) 2021 | Doctoral Symposium
September 28 – October 01, 2021 | Istanbul, Turkey
Information about the individual papers can be found below.
CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming
Reza Farahani (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)
Abstract: Video streaming has become one of the most prevailing, bandwidth-hungry, and latency-sensitive Internet applications. HTTP Adaptive Streaming (HAS) has become the dominant video delivery mechanism over the Internet. Lack of coordination among the clients and lack of awareness of the network in pure client-based adaptive video bitrate approaches have caused problems, such as sub-optimal data throughput from Content Delivery Network (CDN) or origin servers, high CDN costs, and non-satisfactory users’ experience. Recent studies have shown that network-assisted HAS techniques by utilizing modern networking paradigms, e.g., Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing can significantly improve HAS system performance. In this doctoral study, we leverage the aforementioned modern networking paradigms and design network assistance for/by HAS clients to improve HAS systems performance and CDN/network utilization. We present four fundamental research questions to target different challenges in devising a network-assisted HAS system.
Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming
Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)
Abstract: Video traffic comprises the majority of today’s Internet traffic, and HTTP Adaptive Streaming (HAS) is the preferred method to deliver video content over the Internet. Increasing demand for video and the improvements in the video display conditions over the years caused an increase in the video coding complexity. This increased complexity brought the need for more efficient video streaming and coding solutions. The latest standard video codecs can reduce the size of the videos by using more efficient tools with higher time-complexities. The plans for integrating machine learning into upcoming video codecs raised the interest in applied machine learning for video coding. In this doctoral study, we aim to propose applied machine learning methods to video coding, focusing on HTTP adaptive streaming. We present four primary research questions to target different challenges in video coding for HTTP adaptive streaming.
Multi-access Edge Computing for Adaptive Bitrate Video Streaming
Jesús Aguilar-Armijo (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)
Abstract: Video streaming is the most used service in mobile networks and its usage will continue growing in the upcoming years. Due to this increase, content delivery should be improved as a key aspect of video streaming service, supporting higher bandwidth demand while assuring high quality of experience (QoE) for all the users. Multi-access edge computing (MEC) is an emerging paradigm that brings computational power and storage closer to the user. It is seen in the industry as a key technology for 5G mobile networks, with the goals of reducing latency, ensuring highly efficient network operation, improving service delivery and offering an improved user experience, among others. In this doctoral study, we aim to leverage the possibilities of MEC to improve the content delivery of video streaming services. We present four main research questions to target the different challenges in content delivery for HTTP Adaptive Streaming.
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing and In-Network Intelligence
Alireza Erfanian (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)
Abstract: Live video streaming traffic and related applications have experienced significant growth in recent years. More users have started generating and delivering live streams with high quality (e.g., 4K resolution) through popular online streaming platforms such as YouTube, Twitch, and Facebook. Typically, the video contents are generated by streamers and watched by many audiences, which are geographically distributed in various locations far away from the streamers’ locations. The resource limitation in the network (e.g., bandwidth) is a challenging issue for network and video providers to meet the users’ requested quality. In this thesis, we will investigate optimizing QoE and end-to-end (E2E) latency of live video streaming by leveraging edge computing capabilities and in-network intelligence. We present four main research questions aiming to address the various challenges in optimizing live streaming QoE and E2E latency by employing edge computing and in-network intelligence.
Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)
Abstract: Video streaming services account for the majority of today’s traffic on the Internet. Although the data transmission rate has been increasing significantly, the growing number and variety of media and higher quality expectations of users have led networked media applications to fully or even over-utilize the available throughput. HTTP Adaptive Streaming (HAS) has become a predominant technique for multimedia delivery over the Internet today. However, there are critical challenges for multimedia systems, especially the tradeoff between the increasing content (complexity) and various requirements regarding time (latency) and quality (QoE). This thesis will cover the main aspects within the end user’s environment, including video consumption and interactivity, collectively referred to as player environment, which is probably the most crucial component in today’s multimedia applications and services. We will investigate the methods that can enable the specification of various policies reflecting the user’s needs in given use cases. Besides, we will also work on schemes that allow efficient support for server-assisted, and network-assisted HAS systems. Finally, those approaches will be considered to combine into policies that fit the requirements of all use cases (e.g., live streaming, video on demand, etc.).