A latency compensation technique based on game characteristics to mitigate the influence of delay on cloud gaming quality of experience

Sabet, Saeed Shafiee; Schmidt, Steven; Zadtootaghaj, Saman; Naderi, Babak; Griwodz, Carsten; Möller, Sebastian

doi:10.1145/3339825.3391855

Cited by 32 publications

(11 citation statements)

References 19 publications

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“…Apart from the fact that the CG approach is in general highly interesting from a methodologically perspective due to the advantages mentioned above, the method will be used for the present research for two purposes: a) the development of a questionnaire measuring input quality described in Chapter 5, and b) the investigation of the mediation of game characteristics on the impact of network impairments on gaming QoE reported in [18], [210], [211] as well as Section 6.1 and 6.2.…”

Section: Crowdsourcing For Gaming Qoe Assessmentmentioning

confidence: 99%

“…In the other CS test, referred to as Dataset 4.3 (CS3), the remaining conditions were investigated. However, in the latter test, also some additional conditions were added to investigate several latency compensation techniques using game adaptation which mitigates the influence of delay on QoE [211]. These conditions are excluded from a comparison of the test environments in the following.…”

Section: Data Collectionmentioning

confidence: 99%

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Assessing the Quality of Experience of Cloud Gaming Services

Schmidt¹

2023

T-Labs Series in Telecommunication Services

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Die Videospielebranche hat den größten Unterhaltungssektor unserer Zeit geschaffen, der schnell wächst und das Leben vieler Generationen weltweit beeinflusst. Besonders hochkomplexe Spiele erfordern jedoch, dass Spieler über leistungsstarke Hardware verfügen. So wurde in den letzten Jahren ein neues Konzept namens Cloud Gaming entwickelt, mit dem Nutzer diese Spiele, die auf einem Cloud-Server ausgeführt werden, fernsteuern können. Während der Ansatz verschiedene Vorteile mit sich bringt, stellt die zusätzliche Netzwerkverbindung und Videoverarbeitung viele neue technische Herausforderungen für Netzbetreiber und Dienstanbieter. Um diese Hindernisse zu überwinden und die Zufriedenheit ihrer Kunden zu gewährleisten, haben Unternehmen ein starkes Interesse daran, das Nutzungserleben (Quality of Experience, QoE) der Spieler zu untersuchen und vorherzusagen. Traditionelle QoE-Evaluierungen von Multimediadiensten verwenden kontrollierte subjektive Experimente, bei denen die Teilnehmer gebeten werden, ihre Meinung zu präsentierten Stimuli, z. B. einer Netzwerkbedingung, unter Verwendung validierter Fragebögen nachträglich zu äußern. Um diesen Beurteilungsprozess zu beschreiben, schlagen Möller und Kollegen (2013) eine Taxonomie der Qualitätsaspekte von Cloud-Gaming-Systemen vor. Die Anwendbarkeit etablierter Bewertungsmethoden zur Messung des hochgradig mehrdimensionalen Konstrukts der Gaming-QoE ist jedoch sehr begrenzt. Da sich das Gebiet der Gaming-QoE noch in der Anfangsphase befindet, fehlen validierte Bewertungsmethoden, die speziell für Cloud-Gaming-Dienste entwickelt wurden. Ziel der vorgestellten Forschung ist es daher, eine umfassende, zuverlässige und validierte Bewertungsmethode für die Gaming-QoE für Cloud-Gaming-Dienste zu entwickeln und zu evaluieren. Die Methode ermöglicht die Gestaltung subjektiver Tests zur Erstellung von Datensätzen für Qualitätsvorhersagemodelle und ermöglicht ein detailliertes Verständnis der Beziehungen zwischen einer Vielzahl von Qualitätsaspekten. Als erster Schritt in Richtung eines einheitlichen Evaluierungsansatzes wurden verfügbare Fragebögen auf kompakte Weise zusammengefasst. Da kein Messinstrument zur Bewertung der Interaktionsqualität identifiziert wurde, wurde ein neuer Fragebogen, die Gaming Input Quality Scale (GIPS), entwickelt. Darüber hinaus wurde für dessen Erstellung ein neues Framework zur Bewertung der Gaming-QoE mithilfe eines Crowdsourcing-Ansatzes entworfen. Zuletzt wurde die Evaluierungsmethode basierend auf einem großen Datensatz, der dominante Netzwerk-und Codierungsbedingungen umfasst, unter Verwendung der Strukturgleichungsmodellierung untersucht. Die Forschung ergab, dass das Crowdsourcing-Framework vergleichbare Ergebnisse wie Laborstudien liefern kann. Zudem wurde gezeigt, dass die entwickelte Evaluierungsmethode zuverlässige und gültige Benutzerbewertungen liefert und gleichzeitig ein Testdesign ermöglicht, das zur Entwicklung von Qualitätsvorhersagemodellen geeignet ist. Zusammenfassend sind die Hauptbeiträge der Arbeit (1) eine empirische Validieru...

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Section: Crowdsourcing For Gaming Qoe Assessmentmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Assessing the Quality of Experience of Cloud Gaming Services

Schmidt¹

2023

T-Labs Series in Telecommunication Services

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“…Table 1 provides an overview of existing studies on delay compensation for reducing the effects of delay on gaming systems. There are three main types of network delay compensation approaches: 1) sending additional packets for accurate prediction of the game information [5]- [9], 2) adjusting the game world according to the network delay [10]- [12], and 3) compensation of the game information without additional packets and game world modification [13]- [17]. The existing studies in [13]- [17] proposed delay compensation methods for online games without additional bandwidth consumption or game world modification.…”

Section: A Delay Compensation For Online Multiplayer Gamesmentioning

confidence: 99%

“…Each player device sends packets to the server -including the player's locations and projectiles fired on the game field -60 times per second. The server determines whether each projectile hits the target based on the received [9] Network delay Online gaming (multiplayer shooting) First type: sending motion vectors for prediction [10] Network delay Cloud gaming (singleplayer) Second type: world alternation [11] Network delay Cloud gaming (singleplayer) Second type: adjusting the gravity power and flap power [12] Processing/Network/Local Both (2D moving target selection) Second type: controling motion and input delays [13]- [15] Network delay Online gaming (multiplayer shooting) Third type: Server-side delay compensation [16], [17] Network delay Online gaming (multiplayer) Third type: bucket synchronization, local lag [21] Response delay Both (Massively Multiplayer Online Games (MMOG)) Load prediction and resource allocation [22] Processing delay Cloud gaming Delay-aware resource allocation [23] Delay between edge and local excecutions Both Offloading [24] Queuing…”

Section: Distinguished Feature Of Proposed Reinforcement-based Delay Compensationmentioning

confidence: 99%

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Client-Side Network Delay Compensation for Online Shooting Games

Motoo¹,

Kawasaki

Fujihashi

et al. 2021

IEEE Access

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In online multiplayer shooting games, a long network delay can adversely impact player performance because it leads to large synchronization errors in the game information, such as location errors, between players. To mitigate synchronization errors between players, we propose a novel clientside delay compensation system for online multiplayer shooting games. The proposed delay compensation system comprises regression-based and reinforcement-based methods, which accurately predict present game information based on past game information from within each player's device. Specifically, the regression-based method utilizes linear regression to accurately predict a player's present location based on past location information, whereas the reinforcement-based method utilizes deep reinforcement learning to predict a player's present commands corresponding to the player's current location, based on past snapshots of the game fields. The advantage of the proposed system over the existing server-side delay compensation methods, e.g., Lag Compensation, is that they display accurate game information in each player's device as if the player devices were synchronized with each other; thus, any unfair advantages to the players are prevented. We developed a shooting game simulator to experimentally compare the proposed system with existing ones under constant and fluctuating network delays in loss-free and loss-prone networks. The movement models of different players were considered in the experiments. The results demonstrate that the proposed system simultaneously achieves a lower synchronization error between players and higher shooting accuracy, compared to Lag Compensation. For example, the average decrease in player shooting hit rate under a constant network delay of 100 ms is 0.55, 0.076, and 0.0039 without delay compensation and with the proposed regression-based and reinforcement-based methods, respectively. Under the same network delay, the proposed regression-based and reinforcement-based methods show the improvement of 16 pixels and 14 pixels in terms of the synchronization error compared to Lag Compensation, respectively.

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Influence of Delay on Gaming QoE

Sabet

2022

T-Labs Series in Telecommunication Services

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A latency compensation technique based on game characteristics to mitigate the influence of delay on cloud gaming quality of experience

Cited by 32 publications

References 19 publications

Assessing the Quality of Experience of Cloud Gaming Services

Assessing the Quality of Experience of Cloud Gaming Services

Client-Side Network Delay Compensation for Online Shooting Games

Influence of Delay on Gaming QoE

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