Theses in Progress

Dynamic Adaptive Point Cloud Streaming

June 11, 2019 – ,


As a developing media content, point cloud has gained great interests in both industry and research due to its applicability in diverse areas such as virtual reality, autonomous driving, and video gaming.

High-quality 3D point cloud would enable new ways of interaction with the virtual environments. However, point clouds are highly resource-demanding and therefore are difficult to stream at an acceptable quality level.

In this work, the student will investigate how can DASH and QUIC tackle the intensive bandwidth requirement of point cloud streaming while at the same time maintaining high QoE.


Create and evaluate a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. For that:

  1. Extensive literature and related work on point cloud streaming
  2. Design and implement a point cloud streaming system
    • Extend the concept of dynamic adaptive streaming over HTTP (DASH) to support Point cloud streaming
    • Design QUIC data streams to transmit multiple point cloud objects within a scene
  3. Evaluation based on related work metrics such as latency, playout buffer and video quality
  4. An Introduction for the topic, related works, design, implementation, and evaluation of the proposed system must be described and discussed in the final report


  • Good programming skills in Java or Python
  • Previous experience in DASH and QUIC is very helpful

download corresponding tendering

Keywords: DASH, Point Cloud, Streaming

Research Area(s):

Tutor: Alkhalili, Amr Rizk

Student: Haymanot Mekonen Geberehiwot

Theses in Progress