Learning to Cooperate and Communicate Over Imperfect Channels

Information exchange in multi-agent systems improves the cooperation among agents, especially in partially observable settings. In the real world, communication is often carried out over imperfect channels. This requires agents to handle uncertainty due to potential information loss. In this paper, we consider a cooperative multi-agent system where the agents act and exchange information in a decentralized manner using a limited and unreliable channel. To cope with such channel constraints, we propose a novel communication approach based on independent Q-learning. Our method allows agents to dynamically adapt how much information to share by sending messages of different sizes, depending on their local observations and the channel's properties. In addition to this message size selection, agents learn to encode and decode messages to improve their jointly trained policies. We show that our approach outperforms approaches without adaptive capabilities in a novel cooperative digit-prediction environment and discuss its limitations in the traffic junction environment

Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming

Whereas adaptive video streaming for 2D video is well established and frequently used in streaming services, adaptation for emerging higher-dimensional content, such as point clouds, is still a research issue. Moreover, how to optimize resource usage in streaming services that support multiple content types of different dimensions and levels of interactivity has so far not been sufficiently studied. Learning-based approaches aim to optimize the streaming experience according to user needs. They predict quality metrics and try to find system parameters maximizing them given the current network conditions. With this paper, we show how to approach content and network adaption driven by Quality of Experience (QoE) for multi-dimensional content. We describe components required to create a system adapting multiple streams of different content types simultaneously, identify research gaps and propose potential next steps

Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization

We present a cloud-based approach to transform arbitrarily large terrain data to a hierarchical level-of-detail structure that is optimized for web visualization. Our approach is based on a divide-andconquer strategy. The input data is split into tiles that are distributed to individual workers in the cloud. These workers apply a Delaunay triangulation with a maximum number of points and a maximum geometric error. They merge the results and triangulate them again to generate less detailed tiles. The process repeats until a hierarchical tree of different levels of detail has been created. This tree can be used to stream the data to the web browser. We have implemented this approach in the frameworks Apache Spark and GeoTrellis. Our paper includes an evaluation of our approach and the implementation. We focus on scalability and runtime but also investigate bottlenecks, possible reasons for them, as well as options for mitigation. The results of our evaluation show that our approach and implementation are scalable and that we are able to process massive terrain data

Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming

Whereas adaptive video streaming for 2D video is well established and frequently used in streaming services, adaptation for emerging higher-dimensional content, such as point clouds, is still a research issue. Moreover, how to optimize resource usage in streaming services that support multiple content types of different dimensions and levels of interactivity has so far not been sufficiently studied. Learning-based approaches aim to optimize the streaming experience according to user needs. They predict quality metrics and try to find system parameters maximizing them given the current network conditions. With this paper, we show how to approach content and network adaption driven by Quality of Experience (QoE) for multi-dimensional content. We describe components required to create a system adapting multiple streams of different content types simultaneously, identify research gaps and propose potential next steps

Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming

Whereas adaptive video streaming for 2D video is well established and frequently used in streaming services, adaptation for emerging higher-dimensional content, such as point clouds, is still a research issue. Moreover, how to optimize resource usage in streaming services that support multiple content types of different dimensions and levels of interactivity has so far not been sufficiently studied. Learning-based approaches aim to optimize the streaming experience according to user needs. They predict quality metrics and try to find system parameters maximizing them given the current network conditions. With this paper, we show how to approach content and network adaption driven by Quality of Experience (QoE) for multi-dimensional content. We describe components required to create a system adapting multiple streams of different content types simultaneously, identify research gaps and propose potential next steps

Modeling Quality of Experience for Compressed Point Cloud Sequences based on a Subjective Study

There is growing interest in point cloud content due to its central role in the creation and provision of interactive and immersive user experiences for extended reality applications. However, it is impractical to stream uncompressed point cloud sequences over communication networks to end systems because of their high throughput and low latency requirements. Several novel compression methods have been developed for efficient storage and adaptive delivery of point cloud content. However, these methods primarily focus on data metrics and neglect the influence on the actual Quality of Experience (QoE). In this paper, we conduct a user study with 102 participants to analyze the QoE of point cloud sequences and develop a QoE model that can enhance the quality of point cloud content distribution under dynamic network conditions. Our analysis is based on user opinions regarding two representative point cloud sequences, three different frame rates, three viewing distances, and two state-of-the-art point cloud compression libraries, Draco and V-PCC. The results indicate that the proposed models can accurately predict the users' quality perception, with frame rate being the most dominant QoE factor