Quality of experience driven control of interactive media stream parameters

In recent years, cloud computing has led to many new kinds of services. One of these popular services is cloud gaming, which provides the entire game experience to the users remotely from a server, but also other applications are provided in a similar manner. In this paper we focus on the option to render the application in the cloud, thereby delivering the graphical output of the application to the user as a video stream. In more general terms, an interactive media stream is set up over the network between the user's device and the cloud server. The main issue with this approach is situated at the network, that currently gives little guarantees on the quality of service in terms of parameters such as available bandwidth, latency or packet loss. However, for interactive media stream cases, the user is merely interested in the perceived quality, regardless of the underlaying network situation. In this paper, we present an adaptive control mechanism that optimizes the quality of experience for the use case of a race game, by trading off visual quality against frame rate in function of the available bandwidth. Practical experiments verify that QoE driven adaptation leads to improved user experience compared to systems solely taking network characteristics into account

Adaptive Bitrate Streaming in Cloud Gaming

Cloud gaming streams games as video from a server to a client device making it susceptible to network congestion. Adaptive bitrate streaming estimates network capacity and sets encoding parameters to avoid exceeding the bandwidth of the connection. BBR is a congestion control algorithm as an alternative to current loss-based congestion control. We designed and implemented a bitrate adaptation heuristic based on BBR into GamingAnywhere, an open source cloud gaming platform. We conducted a user study and did objective analysis comparing our modified version to the original. Through our results, we found that our adaptive system was less challenging for players and improved retention rates and that there was no statistically significant difference in visual quality from objective testing

Mukautuvien videon toisto algoritmien evaluointi obiilipilivipelaamisessa

Mobile cloud gaming has recently gained popularity as a result of improvements in the quality of internet connections and mobile networks. Under stable conditions, current LTE networks can provide a suitable platform for the demanding requirements of mobile cloud gaming. However, since the quality of mobile network connections constantly change, the network may be unable to always provide the best possible service to all clients. Thus, the ability to adapt is necessary for a mobile cloud gaming platform in order to compensate for changing bandwidth conditions in mobile networks. One approach for doing this is to change the quality of the video stream to match the available bandwidth of the network. This thesis evaluates an adaptive streaming method implemented on a mobile cloud gaming platform called GamingAnywhere and provides an alternative approach for estimating the available bandwidth by measuring the signal strength values of a mobile device. Experimentation was conducted in a real LTE network to determine the best approach in reconfiguring the encoder of the video stream to match the bandwidth of the network. The results show that increasing the constant-rate-factor parameter of the video encoder by 12 reduces the necessary bandwidth to about half. Thus, changing this video encoder parameter provides an effective means to compensate for significant changes in the bandwidth. However, high values of the constant-rate-factor parameter can considerably reduce the quality of the video stream. Thus, the frame rate of the video should be lowered if the constant-rate-factor already has a high value.Mobiilipilvipelaaminen on viimeaikoina kerännyt suosiota parantuneiden internet yhteyksien ja mobiiliverkkojen ansioista. Normaali olosuhteissa nykyiset LTE verkot tarjoavat sopivan alustan mobiili pilvipelaamisen koviin vaatimuksiin. Mobiiliverkkojen yhteyden laatu kuitenkin vaihtelee jatkuvasti ja kaikille käyttäjille ei voida aina tarjota parasta mahdollista yhteyttä. Mukautuminen vaihtelevaan yhteyden laatuun on siis tarpeellista pilvipelaamisalustalle. Tämän voi tehdä esimerkiksi muuttamalla videon kuvanlaatua sopivaksi käytössä olevaan kaistaan. Tässä työssä arvioidaan GamingAnywhere alustalle toteutettu mukautuva videon toistomenetelmä ja esitellään vaihtoehtoinen tapa arvioida käytettävissä olevaa kaistaa mittaamalla mobiilisignaalin vahvuutta mobiililaitteessa. Aidossa LTE verkossa suoritettujen kokeiden avulla selvitettiin paras tapa konfiguroida video enkooderi mukautumaan käytettävissä olevaan kaistan määrään. Tuloksista selviää, että constant-rate-factor-parametrin arvon nostaminen kahdellatoista laskee tarvittavan kaistan määrän noin puoleen. Se on siis tehokkain tapa mukautua merkittäviin muutoksiin kaistan leveydessä. Liian suuret constant-rate-factor-parametrin arvot kuitenkin heikentävät kuvanlaatua merkittävästi, joten kuvataajuutta voi myös alentaa jos parametrin arvo on jo liian suuri

Foveated Video Streaming for Cloud Gaming

Video gaming is generally a computationally intensive application and to provide a pleasant user experience specialized hardware like Graphic Processing Units may be required. Computational resources and power consumption are constraints which limit visually complex gaming on, for example, laptops, tablets and smart phones. Cloud gaming may be a possible approach towards providing a pleasant gaming experience on thin clients which have limited computational and energy resources. In a cloud gaming architecture, the game-play video is rendered and encoded in the cloud and streamed to a client where it is displayed. User inputs are captured at the client and streamed back to the server, where they are relayed to the game. High quality of experience requires the streamed video to be of high visual quality which translates to substantial downstream bandwidth requirements. The visual perception of the human eye is non-uniform, being maximum along the optical axis of the eye and dropping off rapidly away from it. This phenomenon, called foveation, makes the practice of encoding all areas of a video frame with the same resolution wasteful. In this thesis, foveated video streaming from a cloud gaming server to a cloud gaming client is investigated. A prototype cloud gaming system with foveated video streaming is implemented. The cloud gaming server of the prototype is configured to encode gameplay video in a foveated fashion based on gaze location data provided by the cloud gaming client. The effect of foveated encoding on the output bitrate of the streamed video is investigated. Measurements are performed using games from various genres and with different player points of view to explore changes in video bitrate with different parameters of foveation. Latencies involved in foveated video streaming for cloud gaming, including latency of the eye tracker used in the thesis, are also briefly discussed

Congestion control for cloud gaming over udp based on round-Trip video latency

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWe describe a network congestion control mechanism for cloud gaming (CG) platforms based on the user datagram protocol (UDP). To minimize the contribution of the downstream transmission delay to the total end-To-end latency in the interaction-perception loop, we first define the round-Trip video latency (RTVL) and develop a congestion model. Based on them, we design and implement an adaptation strategy that detects the early stages of congestion to prevent high values of RTVL and network bufferbloat, thus avoiding packet losses. Using data measured from the network, our strategy modifies the target output bitrate of the video encoder to throttle down or upto the data flow sent by the server to the client. In the presence of sudden downstream channel capacity drops of over 40%, our algorithm reactively manages to satisfy the key CG requirements for interactive games by entirely avoiding the packet losses and keeping the RTVL below 100 ms. In reasonably stable network conditions, our algorithm proactively keeps exploring for higher bitrates and building a 'network state dictionary,' due to which it achieves an effective downstream channel capacity use of 95%This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades (AEI/FEDER) of the Spanish Government through the Project ‘‘Open Graphics Gaming Cloud’’ under Grant RTC-2016-5676-7 and the Project ‘‘Immersive Visual Media Environments’’ under Grant TEC2016-7598

Foveated Video Streaming for Cloud Gaming

Good user experience with interactive cloud-based multimedia applications, such as cloud gaming and cloud-based VR, requires low end-to-end latency and large amounts of downstream network bandwidth at the same time. In this paper, we present a foveated video streaming system for cloud gaming. The system adapts video stream quality by adjusting the encoding parameters on the fly to match the player's gaze position. We conduct measurements with a prototype that we developed for a cloud gaming system in conjunction with eye tracker hardware. Evaluation results suggest that such foveated streaming can reduce bandwidth requirements by even more than 50% depending on parametrization of the foveated video coding and that it is feasible from the latency perspective.Comment: Submitted to: IEEE 19th International Workshop on Multimedia Signal Processin

A network analysis on cloud gaming: Stadia, GeForce Now and PSNow

Cloud gaming is a new class of services that promises to revolutionize the videogame market. It allows the user to play a videogame with basic equipment while using a remote server for the actual execution. The multimedia content is streamed through the network from the server to the user. This service requires low latency and a large bandwidth to work properly with low response time and high-definition video. Three of the leading tech companies, (Google, Sony and NVIDIA) entered this market with their own products, and others, like Microsoft and Amazon, are planning to launch their own platforms in the near future. However, these companies released so far little information about their cloud gaming operation and how they utilize the network. In this work, we study these new cloud gaming services from the network point of view. We collect more than 200 packet traces under different application settings and network conditions for 3 cloud gaming services, namely Stadia from Google, GeForce Now from NVIDIA and PS Now from Sony. We analyze the employed protocols and the workload they impose on the network. We find that GeForce Now and Stadia use the RTP protocol to stream the multimedia content, with the latter relying on the standard WebRTC APIs. They result in bandwidth-hungry and consume up to 45 Mbit/s, depending on the network and video quality. PS Now instead uses only undocumented protocols and never exceeds 13 Mbit/s

Network-aware video streaming for future media Internet

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SoC-Cluster as an Edge Server: an Application-driven Measurement Study

Huge electricity consumption is a severe issue for edge data centers. To this end, we propose a new form of edge server, namely SoC-Cluster, that orchestrates many low-power mobile system-on-chips (SoCs) through an on-chip network. For the first time, we have developed a concrete SoC-Cluster server that consists of 60 Qualcomm Snapdragon 865 SoCs in a 2U rack. Such a server has been commercialized successfully and deployed in large scale on edge clouds. The current dominant workload on those deployed SoC-Clusters is cloud gaming, as mobile SoCs can seamlessly run native mobile games. The primary goal of this work is to demystify whether SoC-Cluster can efficiently serve more general-purpose, edge-typical workloads. Therefore, we built a benchmark suite that leverages state-of-the-art libraries for two killer edge workloads, i.e., video transcoding and deep learning inference. The benchmark comprehensively reports the performance, power consumption, and other application-specific metrics. We then performed a thorough measurement study and directly compared SoC-Cluster with traditional edge servers (with Intel CPU and NVIDIA GPU) with respect to physical size, electricity, and billing. The results reveal the advantages of SoC-Cluster, especially its high energy efficiency and the ability to proportionally scale energy consumption with various incoming loads, as well as its limitations. The results also provide insightful implications and valuable guidance to further improve SoC-Cluster and land it in broader edge scenarios