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Latency Optimization in Cloud Gaming Using Edge-Assisted Resource Allocation Techniques

Muhammad Rafay Khalil Chaudhary * , Haider Ejaz Bhatti , Muhammad Noman
* Corresponding author: Muhammad Rafay Khalil Chaudhary ( L1F23BSCS0306@ucp.edu.pk )
Available online October 1, 2026
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Abstract

Cloud gaming, which is a new application of cloud computing, enables players to operate high-performance games on low-specification devices by having the processing done on remote servers. Nevertheless, latency resulting from server overload and network delays is still a significant obstacle to a smooth user experience. The present study looks at the causes of latency in cloud gaming environments and suggests an edge-assisted resource allocation strategy in order to reduce it. In this proposed architecture, edge computing nodes are positioned nearer to the users so that latency-sensitive tasks such as input processing and frame rendering are carried out locally, while more resource-intensive operations like AI calculations and physics simulation are offloaded to the central cloud servers. The resource allocation mechanism dynamically takes into account geographical proximity, the current level of server load, and the predicted user activity when choosing the best edge server for each session. The results indicate that the edge-assisted method greatly reduces average latency when compared with systems that rely solely on the cloud (25 ms compared to 120 ms) and enhances Quality of Experience (QoE) under different bandwidth conditions. The findings prove that combining proximity-based routing, dynamic load balancing, and predictive resource allocation provides a scalable and practical way of improving responsiveness and the overall service quality in future cloud gaming platforms.

Keywords

Cloud gaming edge computing resource allocation latency quality of experience

Introduction

Cloud computing provides on-demand access to scalable infrastructure over the Internet [14]. One of its most demanding applications is cloud gaming, in which game logic and rendering run on remote servers and the resulting video is streamed to the player in real time [4, 2]. This removes the need for expensive client hardware, but it makes the user experience depend on the network and the server. The main obstacle is latency, i.e., the delay between a player’s input and the corresponding response on screen as shown in Figure 1. Even small increases in delay measurably reduce player performance, especially in fast-paced games [1], and measurement studies show that cloud-to-end-user latency is a limiting factor for deployment [5]. Server workload, network congestion, and limited bandwidth further degrade perceived quality [3, 6]. Edge computing places computational resources closer to end users and reduces transmission delay [7, 8]. Several works apply it to cloud gaming [9, 10, 11, 12], and offloading has been studied as a way to balance local and remote computation [13]. Most existing designs, however, do not jointly consider proximity, instantaneous load, and predicted demand in a simple, transparent decision rule.

The main contributions of this paper are as follows:

  • We partition the gaming process so that latency-critical functions (input handling, frame rendering, encoding, input prediction) run at the edge, while compute-intensive and globally coordinated functions run in the cloud.

  • We formulate a scoring rule that combines user-edge proximity, current node load, and predicted demand, and give the decision procedure as an algorithm (Algorithm [alg:select]).

  • We define a task-level offloading rule that uses live measurements of latency, jitter, packet loss, and throughput to decide between edge and cloud execution.

  • Under an explicitly stated set of assumed parameters, we compare the proposed scheme with a cloud-only baseline in terms of latency and QoE under varying user counts and bandwidth, and discuss the limitations of this evaluation.

The rest of the paper is organized as follows. Section 2 reviews related work, Section 3 states the problem and research gap, Section 4 presents the architecture, Section 5 the allocation strategy, Section 6 the workflow, Section 7 the evaluation setup, Section 8 the results, and Sections 9 and 10 the limitations and conclusion.

Complete Article

The complete article, including all figures, tables, equations and algorithms, is available in the official publication PDF.

Conclusion

This paper proposed an edge-assisted resource allocation strategy for cloud gaming. Latency-critical tasks run at nearby edge nodes, a proximity/load/prediction score selects the serving node, and a feedback-driven rule offloads heavy tasks to the cloud. The illustrative evaluation, based on assumed parameters, indicates lower latency and higher QoE than a cloud-only system. Future work includes validation through simulation and real testbeds, comparison with additional baselines, cost-aware allocation, and learning-based prediction and scheduling.

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