Feasibility study of stochastic streaming with 4K UHD video traces

Kim, Joongheon; Ryu, Eun‐Seok

doi:10.1109/ictc.2015.7354813

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…In practice, the channel condition between the user and the caching node delivering the desired video could be varying due to Doppler shift as the user is moving, but this effect is not captured in this paper. Peak-signal-to-noise ratio (PSNR) is considered as a video quality measure, and quality measures and file sizes depending on quality levels are supposed as P(q) = [34, 36.64, 39.11] dB and N (q) = [2621, 5073, 10658] Kbits which are obtained from real-world video traces [36]. Since we assume that η min = 0.99 and t 0 W log 2 (1+γ min ) = N (1), the minimum intensity of PPP distributions to satisfy the performance criterion of {γ min , η min } should be λ min = 0.1113.…”

Section: Simulation Resultsmentioning

confidence: 99%

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

Choi

et al. 2019

IEEE Trans. Wireless Commun.

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This paper proposes a video delivery strategy for dynamic streaming services which maximizes time-average streaming quality under a playback delay constraint in wireless caching networks. The network where popular videos encoded by scalable video coding are already stored in randomly distributed caching nodes is considered under adaptive video streaming concepts, and distance-based interference management is investigated in this paper. In this network model, a streaming user makes delay-constrained decisions depending on stochastic network states: 1) caching node for video delivery, 2) video quality, and 3) the quantity of video chunks to receive. Since wireless link activation for video delivery may introduce delays, different timescales for updating caching node association, video quality adaptation, and chunk amounts are considered. After associating with a caching node for video delivery, the streaming user chooses combinations of quality and chunk amounts in the small timescale.The dynamic decision making process for video quality and chunk amounts at each slot is modeled using Markov decision process, and the caching node decision is made based on the framework of Lyapunov optimization. Our intensive simulations verify that the proposed video delivery algorithm works reliably and also can control the tradeoff between video quality and playback latency. Index TermsWireless caching network, video delivery, stochastic network optimization, Lyapunov optimization, Markov decision process M. Choi and J. Kim are with the

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Section: Simulation Resultsmentioning

confidence: 99%

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

Choi

et al. 2019

IEEE Trans. Wireless Commun.

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“…where p n (t), q n (t), and D n (t) denote the avatar popularity, age-of-information, and the accumulated data size in the n-th physical space at time-step t, respectively. Furthermore, f c (•) represents a quality function as introduced by the authors in [44]. Note that the age-of-information q n (t) adheres to a time-dependent and non-zero monotonic increasing function.…”

Section: Qrl-based Spatio-temporal Prioritization 1) Spatial-prioriti...mentioning

confidence: 99%

“…In our metaverse system, the collected data from physical-space are transmitted to the central server . The collected data can be sent to the server with varying levels of quality, represented by QP values of {22, 27, 32, 37} [44]. For rendering meta-space, the corresponding bit rates for each QP are 26, 496 Kbps, 10, 658 Kbps, 5, 073 Kbps, and 2, 621 Kbps [44].…”

Section: Performance Evaluation a Experimental Settingmentioning

confidence: 99%

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Park,

Baek,

Kim

2024

IEEE Access

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A metaverse is composed of a physical-space and virtual-space, with the aim of having users in both the virtual reality and the real world experience. Prioritization is essential, but it is not straight-forwarded due to the limitation of computing resources in real-world, making it impossible to synchronize all data. Therefore, it is crucial to allocate resources based on regional preferences in physical-space and content preferences of users in virtual-space. The referencing system consists of two-stage sub-computations, (i) spatial-prioritization for more data gathering under the consideration of avatar-popularity one top of physical-space and (ii) temporal-prioritization for virtual-space rendering under the avatar-popularity. Both prioritization tasks are combinatorics problems and are well-known NP-hard. The problem scale is also large, making it difficult to solve within given times. The classical deep learning cannot be the solution. Quantum-based learning algorithms can be the potential solutions due to high-performance computing capabilities, because a small number of qubits can represent an exponentially large amount of information. On top of these advantages, an improved quantum reinforcement learning (QRL) algorithm is proposed for reducing the control dimensions into a logarithmic scale. We corroborate that our proposed QRL-based algorithm for low-dimensional spatio-temporal prioritization improves convergence and performance.

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“…Let N rc = N rc (1)∪ N rc (2) ∪ · · · ∪ N rc (J). Then, (41) can be re-written as n∈N −Nrc g n (α n (t), t) + J j=1 n∈Nrc(j) g n (α n (t), t).…”

Section: Dynamic Node Association For Video File Delivery Under Qumentioning

confidence: 99%

“…end if 13: end for 14: end while measure, and according to [41], quality measures and file sizes depending on quality levels are P(q) = [34, 36.64, 39.11] dB and L(q) = [2621, 5073, 10658] kbits, respectively. Especially for finding the optimal caching probabilities, the approximately normalized file size M q = [1, 2, 4] is used.…”

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confidence: 99%

Wireless Video Caching and Dynamic Streaming Under Differentiated Quality Requirements

Choi

Kim

Moon

2018

IEEE J. Select. Areas Commun.

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This paper considers one-hop device-to-device (D2D)-assisted wireless caching networks that cache video files of varying quality levels, with the assumption that the base station can control the video quality but cache-enabled devices cannot. Two problems arise in such a caching network: file placement problem and node association problem. This paper suggests a method to cache videos of different qualities, and thus of varying file sizes, by maximizing the sum of video quality measures that users can enjoy. There exists an interesting trade-off between video quality and video diversity, i.e., the ability to provision diverse video files. By caching high-quality files, the cacheenabled devices can provide high-quality video, but cannot cache a variety of files. Conversely, when the device caches various files, it cannot provide a good quality for file-requesting users. In addition, when multiple devices cache the same file but their qualities are different, advanced node association is required for file delivery. This paper proposes a node association algorithm that maximizes time-averaged video quality for multiple users under a playback delay constraint. In this algorithm, we also consider request collision, the situation where several users request files from the same device at the same time, and we propose two ways to cope with the collision: scheduling of one user and non-orthogonal multiple access. Simulation results verify that the proposed caching method and the node association algorithm work reliably.

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Feasibility study of stochastic streaming with 4K UHD video traces

Cited by 7 publications

References 9 publications

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Wireless Video Caching and Dynamic Streaming Under Differentiated Quality Requirements

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