Removing human players from the loop: AI-assisted assessment of Gaming QoE

Sviridov, German; Beliard, Cedric; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario

doi:10.1109/infocomwkshps50562.2020.9162916

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Future directions include studying their infrastructures from different points of view worldwide to find similarities and differences in the deployments. Moreover, we believe that it is crucial to conduct campaigns aiming to measure the subjective quality of experience (QoE) enjoyed by the users with human-in-the-loop controlled experiments, and with AI-bot players [30]. Finally, Microsoft and Amazon will release their cloud gaming platforms, namely xCloud, and Luna, which are in early deployment at the time of writing this article.…”

Section: Discussionmentioning

confidence: 99%

“…In the case where available bandwidth is in Mbit/s, we conclude that a user could only reach a video resolution of 720p. When bandwidth is in the range [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] Mbit/s, a better 1080p video could be transmitted, while more than 44 Mbit/s allow the user to receive a 4K quality video properly.…”

Section: Cloud Gaming Under Mobile Networkmentioning

confidence: 99%

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A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow

Domenico

Perna

Trevisan

et al. 2021

Network

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Cloud gaming is a class of services that promises to revolutionize the videogame market. It allows the user to play a videogame with essential equipment while using a remote server for the actual execution. The multimedia content is streamed through the network from the server to the user. Hence, 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 products, and others, like Microsoft and Amazon, are also launching their platforms. However, these companies have released little information about their cloud gaming operation and how they utilize the network. In this work, we study cloud gaming services from the network point of view. We collect more than 200 packet traces under different application settings and network conditions from a broadband network to poor mobile 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 that 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. Depending on the network and video quality, they result in bandwidth-hungry services consuming up to 45 Mbit/s. PS Now instead uses only undocumented protocols and never exceeds 13 Mbit/s. 4G mobile networks can often sustain these loads, while traditional 3G connections struggle. The systems quickly react to deteriorated network conditions, and packet losses up to 5% do not cause a reduction in resolution.

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Section: Discussionmentioning

confidence: 99%

Section: Cloud Gaming Under Mobile Networkmentioning

confidence: 99%

A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow

Domenico

Perna

Trevisan

et al. 2021

Network

View full text Add to dashboard Cite

show abstract

“…Future directions include the study of their infrastructures from different points of view around the world with the goal of finding similarities and differences in the deployments. Moreover, we believe it is important to conduct campaigns aiming at measuring the subjective Quality of Experience (QoE) enjoyed by the users with human-in-theloop controlled experiments and also with AI-bot players [24]. Finally, Microsoft and Amazon are going to release their cloud gaming platforms, namely xCloud and Luna, which are in early deployment at the time of writing this article.…”

Section: Discussionmentioning

confidence: 99%

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

Di Domenico,

Perna,

Trevisan

et al. 2020

Preprint

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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 among 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 bandwidthhungry 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.

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“…In this context, AI agents are employed to reach an objective related to QoS (reducing flow completion time in DCN [63], improving signal quality in WLAN [116], [117]) or QoE (e.g. of videos [118] and games [119]). To attain such goal, agents receive a reward as a result their action (e.g., setting threshold for ECN marking [63], WLAN channel [116] or power [117] configurations, CDN node selection [118], or relative priority of game traffic [119]).…”

Section: Learning To Learn (L4 and Beyond)mentioning

confidence: 99%

“…of videos [118] and games [119]). To attain such goal, agents receive a reward as a result their action (e.g., setting threshold for ECN marking [63], WLAN channel [116] or power [117] configurations, CDN node selection [118], or relative priority of game traffic [119]). In all these disparate cases cases, AI is used to guide the exploration of an otherwise very large state space: e.g., from simpler Stochastic Bandits used in [118], to more complex Deep Reinforcement Learning (DRL) in [63], [116], or Transformers in [117].…”

Section: Learning To Learn (L4 and Beyond)mentioning

confidence: 99%

Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks

Rossi,

Zhang

2022

Preprint

Self Cite

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The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks.

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Removing human players from the loop: AI-assisted assessment of Gaming QoE

Cited by 6 publications

References 10 publications

A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow

A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow

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

Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks

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