Qoe-based rate adaptation scheme selection for resource-constrained wireless video transmission

Thakolsri, Srisakul; Kellerer, Wolfgang; Steinbach, Eckehard

doi:10.1145/1873951.1874077

Cited by 21 publications

(17 citation statements)

References 7 publications

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“…Novel packet scheduling approaches address this issue, especially in video services [11,28,29], since they propose content-aware dropping schemes that aim to optimize the overall QoE in wireless networks in the presence of constrained resources. In [5], the scheduler calculates how many packets need to be dropped, and selects the combination of packets to drop that minimizes the negative QoE impact on all flows.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, we consider a failed rate switch up scenario that occurs whenever a bad QoE feedback (QoE t s→d < QoE Thr ) is received immediately after an instance where a rate switch up was performed (Lines [25][26][27][28][29]. In this situation, we perform a rate switch down with the same decay factor α i+1 and increase the backoff level B t j+1 .…”

Section: Source Rate Controlmentioning

confidence: 99%

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Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking

et al. 2015

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Section: Related Workmentioning

confidence: 99%

Section: Source Rate Controlmentioning

confidence: 99%

Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking

et al. 2015

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“…However, subjective test campaigns are costly to conduct and require time. Researchers have thus worked on QoE models that map objective non-perceptual video quality metrics into MOS values [15][16][17]. The idea is that the combination of several paramaters (such as video bit-rate, packet loss, and video genre) can lead to a good estimation of the MOS.…”

Section: A User Satisfactionmentioning

confidence: 99%

Optimal Delivery of Rate-Adaptive Streams in Underprovisioned Networks

Liu

Rosenberg

Simon

et al. 2014

IEEE J. Select. Areas Commun.

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Abstract-The growth of Internet video traffic imposes a severe capacity problem in today's Content Delivery Network (CDN). Rate-adaptive streaming technologies, such as the Dynamic Adaptive Streaming over HTTP (DASH) standard, reinforces this problem in the core CDN infrastructure since delivering one video means delivering multiple representations for an aggregated bit-rate that is commonly over 10 Mbps. In this paper, we explore better trade-offs between CDN infrastructure cost and Quality of Experience (QoE) of the end-users for live broadcast video streaming applications. We consider in particular underprovisioned CDN networks, our goal being to maximize the QoE for the population of heterogeneous end-users despite the lack of resources in the intermediate CDN equipments. We show that previous theoretical models based on elastic bit-rates do not fit for this context. We propose a user-centric discretized streaming model where the satisfaction of end-users is related to the context and where a stream has to be either delivered in its entirety, or not delivered at all. We first formulate an Integer Linear Program (ILP) that achieves the optimal delivery through a multi-tree delivery overlay. The evaluation of the ILP shows the benefits of this model. We then design a practical system by revisiting the three main algorithms implemented in CDN: userto-server assignment, content placement and content delivery. At last, we use a realistic trace-driven large-scale simulator to study the performances of our system. In particular, we show that the population of users is reasonably well served (three quarters of the population do not experience degradation) even when the CDN infrastructure experiences a severe underprovisioning (less than half of the required infrastructure).

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“…On a different track, researches like [5] and [6] have been devoted to solve how to maximize the content and the network resources under different QoE requirements. On the other side, researches like [1] mainly contributed to the maximization of QoE with the caching of different bit rates, only accounting for the required bit rate.…”

Section: Introductionmentioning

confidence: 99%

A New QoE-Driven Video Cache Allocation Scheme for Mobile Cloud Server

Zhou

Sun

Wang

et al. 2015

Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustn

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Abstract--With the advent of mobile cloud computing, video cache technologies at local cellular networks have attracted extensive attention. Nevertheless, existing video cache allocation schemes mostly made decisions only according to the video coding requirements, without considering users' individual perception for the video service. In this paper, we propose a new video cache allocation scheme with the consideration of quality of experience (QoE) of users under limited storage space. We make use of the linear regression algorithm to map the relationship between the requested video rate, the replied video rate, the channel condition and the QoE value, which then helps to obtain the different video rates to be stored in the server. Meanwhile, we define the parameter to represent the popularity of a video clip. We optimize the cache space allocation for each video clip based on these parameters in the mobile cloud server of local cellular networks. The experiments demonstrate that the proposed scheme has a better performance in terms of the overall QoE of users with the constraint of the total cache size.

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Qoe-based rate adaptation scheme selection for resource-constrained wireless video transmission

Cited by 21 publications

References 7 publications

Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking

Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking

Optimal Delivery of Rate-Adaptive Streams in Underprovisioned Networks

A New QoE-Driven Video Cache Allocation Scheme for Mobile Cloud Server

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