Showing papers by "Carsten Griwodz published in 2019"

Influence of primacy, recency and peak effects on the game experience questionnaire

Abstract: When a participant is asked to evaluate a stimulus, the judgment is based on the remembered experience, which might be different from the actual experience. This phenomenon happens according to the theory that some moments of an experience such as the beginning, peak and the end of the experience have more impact on the memory. These moments can be recalled with a higher probability than the other parts of the experience, and some minor bad moments of experience might be forgotten or forgiven due to the rest of the good experiences. This paper, using a subjective study and emulating an artificial delay on participants' gameplay investigates the influence of these serial-position effects on the Game Experience Questionnaire (GEQ). The result shows that GEQ does not suffer from either recency, primacy or peak effects. However, when users are asked about the controllability and responsiveness of the games, the recency effect exists. The paper also shows that GEQ has the forgiveness effect and participants forgive or may forget a bad experience if it coincides with a considerable duration of a good experience.

Towards the Impact of Gamers' Adaptation to Delay Variation on Gaming Quality of Experience

Abstract: Both online and cloud gaming services require a very low network delay to create a good Quality of Experience (QoE) for their users. The required network latency cannot be guaranteed due to the current best effort-nature of the network, and as a result, network latency often degrades the gamer’s performance and QoE. In this paper, the adaptability of gamers to different variations on delay is investigated both subjectively and objectively using three self-developed games. The results show that gamers can adapt to constant delay while they are playing and change their behavior if the actions in a game are predictable. Such adaptation leads to a significant increase in gamers performance and QoE. The paper also provides evidence that regardless of performance frequent delay switching annoys gamers. The result of this study can be used to create a network resource allocation technique which controls a congested network by giving more priority and resource to the unadaptable games than the adaptable games.

RATS: adaptive 360-degree live streaming

Abstract: Recent approaches to tiled 360° adaptive bitrate video streaming present significant bandwidth savings at little risk of stalling when only parts of the video, e.g., the current and predicted viewport, are transferred in high quality while the rest of the 360° video tiles are transferred in a lower quality. While this is currently feasible for video on demand scenarios, it poses a difficult problem for 360° live streaming as naive methods produce a considerable overhead owing to the lack of tiling support in existing hardware encoders. In this demo, we show Real-time Adaptive Three-sixty Streaming, or RATS, where we utilize GPU-based HEVC encoding to tile, encode, and stitch 360° video at different qualities in real-time. We show measurement results for the encoding speed, amont of output data, and output quality for different tiling configurations. While we observe an increase in both encoding time and output file size with the number of desired tile columns, we also see that real-time encoding is ensured for all considered tiling configurations.

Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks

Abstract: Video streaming is one of the top traffic contributors in the Internet and a frequent research subject. It is expected that streaming traffic will grow 4-fold for video globally and 9-fold for mobile video between 2017 and 2022. In this paper, we present an automatized measurement framework for evaluating video streaming QoE in operational broadband networks, using headless streaming with a Docker-based client, and a server-side implementation allowing for the use of multiple video players and adaptation algorithms. Our framework allows for integration with the acsMONROE testbed and Bitmovin Analytics, which bring on the possibility to conduct large-scale measurements in different networks, including mobility scenarios, and monitor different parameters in the application, transport, network, and physical layers in real-time.

A Deep Learning Approach to Dynamic Passive RTT Prediction Model for TCP

Abstract: The Round-Trip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The end-to-end RTT value influences significantly the dynamics and performance of TCP, which is by far the most used communication protocol. Thus, in communication networks, RTT is an important network performance variable. By measuring the traffic at an intermediate node, a network operator or service provider can estimate the RTT and use the estimation to study and troubleshoot the per-connection characteristics and performance. This paper aims at improving the accuracy and timeliness of the RTT estimation, to help network operators improving their analysis. We propose and evaluate a novel deep learning-based model capable of dynamically predicting at real-time the RTT between the sender and receiver with high accuracy based on passive measurements collected at an intermediate node, taking advantage of the commonly used TCP timestamps. We validate extensively our prediction methodology in a controlled experimental testbed and in a realistic scenario on the Google Cloud platform. We show that our model, which is based on classical deep learning algorithms, gives reasonably effective state-of-the-art performance results across multiple TCP congestion control variants. We also show that the model works well for transfer learning. Even though the RTT prediction model was trained on an emulated network, it performs well also when applied to a realistic scenario setting, as demonstrated in our experimental evaluation.

Evaluation Framework for Real-Time Adaptive 360-Degree Video Streaming over 5G Networks

Abstract: The end-to-end distribution of real-time 360-degree video needs to be evaluated in a wholesome manner, considering all aspects from video capture to encoding, delivery, and playback, as well as timely and appropriate analytics, with a focus on end-user Quality of Experience (QoE). This requires a measurement framework which allows for the collection of metrics from multiple dimensions at the same time. In this work, we propose such a framework for evaluating real-time adaptive 360\degree video streaming over an experimental Fifth Generation (5G) network, which allows for the investigation of different aspects of the end-to-end video delivery chain.

Chaining of hardware accelerated Virtual Network Functions in PCIe Environments

Abstract: The growth of compute-intensive applications causes an increasing demand for computing resources in both data centers and underlying networks. To satisfy these computing and networking demands, hardware-accelerated computing with GPUs and FPGAs becomes more and more prevalent. However, current approaches of accelerated Virtual Network Functions (VNF), typically based on PCIe accelerator cards, suffer from many superfluous data transfers between the memory of hosts and accelerator devices.In this work we propose "host bypassing" for chained hardware-accelerated network functions, which reduces memory copying to a minimum, which increases throughput and reduces latency.

Poster: QoE-Based Analysis of Real-Time Adaptive 360-Degree Video Streaming

Abstract: We propose a QoE-based analysis approach for real-time adaptive 360-degree video streaming measurements, focusing on the correlation between objective video metrics and subjective end-user scores.