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Boos, Kevin; Chu, David; Cuervo, Eduardo

doi:10.1145/2906388.2906418

Cited by 116 publications

(17 citation statements)

References 26 publications

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“…Indeed, supporting the high-data-rate wireless connectivity with low latency necessitated for such data-intensive applications calls for more advanced solutions than merely increasing the available bandwidth [4]. Meanwhile, improving caching and computing capabilities at end-users has been deemed highly effective in increasing the transmission efficiency [7]- [9]. As such, upcoming mobile broadband applications rely heavily on asynchronous content reuse [10], and hence, proactive caching of popular content at the end-users could relieve network congestion and bandwidth consumption during peak traffic demand times [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Antenna Coded Caching for Location-Dependent Content Delivery

Mahmoodi

Salehi

Tölli

2024

IEEE Trans. Wireless Commun.

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Human-computer interaction continuously evolves towards a genuinely immersive experience, submerging users in a three-dimensional (3D) virtual world. A realistic, immersive experience necessitates a highly reliable and agile wireless connection to support immense data transmission. Yet, there are abundant but underutilized memory resources available at the devices which can be harnessed as supplementary assets to reduce the excessive burden on the wireless medium. What is more, the use of Coded Caching (CC) techniques enables the cumulative cache memory of users in the network to be used as an additional communication resource. To this end, a location-dependent multi-antenna CC-based content delivery scheme tailored specifically for wireless extended reality applications is proposed in this paper. First, a novel memory allocation process is developed, enabling an appropriate trade-off between local and global caching gains. In this regard, the local caching gain is maximized when the memory is mostly dedicated to locations with poor connectivity conditions (absolute fairness). In contrast, the global caching gain is maximized when the memory is uniformly allocated among all the locations. As a result of the memory allocation process, unequal fractions of location-dependent multimedia content are cached by each user. Given the asymmetric cache placement, a novel algorithm is proposed to create suitable codewords for each user during the subsequent delivery phase, which simultaneously achieves a global and local caching gain. The proposed delivery scheme also combines global caching and spatial multiplexing gains using a weighted max-min multicast beamformer design with multi-rate modulation. Numerical experiments and mathematical analysis demonstrate significant performance gains, in terms of the 95-percentile expected delivery time, compared to unicast and multicast scenarios where either the local or global caching gain is maximized.

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Section: Introductionmentioning

confidence: 99%

Multi-Antenna Coded Caching for Location-Dependent Content Delivery

Mahmoodi

Salehi

Tölli

2024

IEEE Trans. Wireless Commun.

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“…For example, some studies reduce the rendering delay by introducing the cache layer of panoramic images, and adopt the mechanism of multi-level index table to speed up the search of corresponding FOV images. 11 Some studies focus on hierarchical collaborative rendering of foreground part and background part. The background part with high predictability but heavy rendering load is deployed on the server for pre-rendering, and the foreground part with difficult to predict but light rendering load is deployed on the terminal device for real-time rendering.…”

Section: Related Workmentioning

confidence: 99%

Joint optimization of task scheduling and computing resource allocation for VR video services in 5G‐advanced networks

Huang,

Du,

Yang

et al. 2023

Trans Emerging Tel Tech

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With the development of 5G networks and related hardware equipment, the demand for various VR video services including panoramic video live broadcast and immersive game is increasing day by day. Due to the explosive growth of data volume and the trend toward lightweight VR equipment, traditional local computing architecture faces problems of insufficient computing power and high cost. As an emerging computing paradigm, the remote computing architecture of cloud‐edge collaboration frees users from cumbersome local restrictions. However, edge computing resources are often limited, and the remote cloud has inconsistent network latency. Therefore, how to adaptively schedule and allocate computing resources is the key to determining VR service quality. Based on an in‐depth analysis of the characteristics of VR video services and network environments, this article first builds a cloud‐edge‐network coorperation computing and transmission architecture for VR video services. This architecture can adaptively offload concurrent computing tasks and allocate computing power to them in VR scenes based on network conditions provided by the 5G‐Advanced networks via network information exposure interface. This paper proposes a general optimization problem to maximize user experience. And a single‐slot delay optimization model was constructed. Combining convex optimization theory and deep learning algorithms, this paper designs a task scheduling and computing power allocation strategy based on deep reinforcement learning. The policy gradually learns optimal task scheduling and resource allocation decisions to maximize delay‐related rewards in the long run. This article further introduces the graph neural network under the attention mechanism to solve the problem of changes in the number of concurrent tasks. Finally, through Cloudsim simulation, this article verifies the superiority of task scheduling and computing power allocation strategies based on deep reinforcement learning compared to other common strategies in terms of resource utilization, client latency, scalability and decision time.

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“…In the context of metaverse VR applications, apart from many studies on its enabling technologies [18,27,38,48] such as AR/VR [1,24,26,29,30,34,42], motion sensing [3], and platform governance [9,10,19], their architectural problems and scalability issues to be solved by metaverse developers (e.g., bandwidth consumptions with increasing number of users, CPU/GPU utilization of terminal device, and rendering cost of user avatars) have been studied in [13,14]. The authors of [14] have also pointed out that one critical scalability issue that could be solved by changing the operational architecture of metaverses is that the consumptions of user bandwidth and CPU/GPU resources currently depend on the number of active users, thus, can introduce high overhead on the VR headset -one potential solution is remote rendering, i.e., rendering application graphics on the cloud server instead of on user VR devices.…”

Section: Related Workmentioning

confidence: 99%

MetaVRadar: Measuring Metaverse Virtual Reality Network Activity

Lyu,

Tripathi,

Sivaraman

2023

Proc. ACM Meas. Anal. Comput. Syst.

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The "metaverse", wherein users can enter virtual worlds to work, study, play, shop, socialize, and entertain, is fast becoming a reality, attracting billions of dollars in investment from companies such as Meta, Microsoft, and Clipo Labs. Further, virtual reality (VR) headsets from entities like Oculus, HTC, and Microsoft are rapidly maturing to provide fully immersive experiences to metaverse users. However, little is known about the network dynamics of metaverse VR applications in terms of service domains, flow counts, traffic rates and volumes, content location and latency, etc., which are needed to make telecommunications network infrastructure "metaverse ready" to support superlative user experience in the coming future. This paper is an empirical measurement study of metaverse VR network behavior aimed at helping telecommunications network operators better provision and manage the network to ensure good user experience. Using illustrative hour-long network traces of metaverse sessions on the Oculus VR headset, we first develop a categorization of user activity into distinct states ranging from login home to streetwalking and event attendance to asset trading, and undertake a detailed analysis of network traffic per state, identifying unique service domains, protocols, flow profiles, and volumetric patterns, thereby highlighting the vastly more complex nature of a metaverse session compared to streaming video or gaming. Armed with the network behavioral profiles, our second contribution develops a real-time methodMetaVRadar to detect metaverse session and classify the user activity state leveraging formalized flow signatures and volumetric attributes. Our third contribution practically implementsMetaVRadar, evaluates its accuracy in our lab environment, and demonstrates its usability in a large university network so operators can better monitor and plan resources to support requisite metaverse user experience.

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FlashBack

Cited by 116 publications

References 26 publications

Multi-Antenna Coded Caching for Location-Dependent Content Delivery

Multi-Antenna Coded Caching for Location-Dependent Content Delivery

Joint optimization of task scheduling and computing resource allocation for VR video services in 5G‐advanced networks

MetaVRadar: Measuring Metaverse Virtual Reality Network Activity

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