QoE-Based Cross-Layer Optimization of Wireless Video with Unperceivable Temporal Video Quality Fluctuation

Thakolsri, Srisakul; Kellerer, Wolfgang; Steinbach, Eckehard

doi:10.1109/icc.2011.5963296

Cited by 56 publications

(53 citation statements)

References 9 publications

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“…As an example, for the case with one dominant interfering base station (BS 1 ) to the video user and assuming fixed channel gains, PLR can be written as (11). If the video user experiences interference from multiple independent bursty interferers, the burstiness and hence the PLR at α i = 1 is expected to decrease.…”

Section: A Unicast Streamingmentioning

confidence: 99%

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Interference Shaping for Improved Quality of Experience for Real-Time Video Streaming

Singh

Andrews

Veciana

2012

IEEE J. Select. Areas Commun.

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Abstract-The unpredictability of the wireless medium poses a major challenge to delivering a high quality of experience (QoE) for real-time video services. Bursty co-channel interference is a prominent cause of wireless throughput variability, which leads to video QoE degradation, even for a fixed average channel quality. In this paper, we propose and analyze a network-level resource management algorithm termed interference shaping to smooth out the throughput variations (and hence improve the QoE) of video users by decreasing the peak rate of co-channel best effort users. Wireless link capacity variations are mapped to the realtime video packet loss rate, and the interference shaping QoE gain for video users is quantified by benchmarking against a modified multi-scale structural similarity (H-MS-SSIM) index. H-MS-SSIM is an accurate perceptual video quality metric that incorporates the important hysteresis effect whereby the current QoE (which is subjective) may strongly depend on the recent past. The proposed technique increases mean QoE and reduces the QoE variability over time, with a net perceptual increase of about 2-3x in illustrative settings while incurring insignificant decrease in the QoE for co-channel best effort users. Interference shaping can be implemented in both unicast and multicast real-time video streaming with much higher potential gains for multicast.Index Terms-Quality of experience; resource allocation and interference management; cross-layer design; bursty interference, heterogeneous networks.

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Section: A Unicast Streamingmentioning

confidence: 99%

“…Temporal quality fluctuations were incorporated in utility function for resource allocation in [11]. A general network utilization maximization framework to incorporate the variations in utility functions was proposed in [12].…”

Section: A Related Workmentioning

confidence: 99%

Interference Shaping for Improved Quality of Experience for Real-Time Video Streaming

Singh

Andrews

Veciana

2012

IEEE J. Select. Areas Commun.

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“…[21] Wireless Network [12], [26], [27], [28] [18], [19] [20], [29], [30] Next Generation Network [15] [22]…”

Section: Research Issuementioning

confidence: 99%

Quality-of-Experience (QoE) in Emerging Mobile Social Networks

Dong

Kimata

Sugiura

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYMobile social networks (MSN) provides diverse services to meet the needs of mobile users, i.e., discovering new friends, and sharing their pictures, videos and other information among their common interest friends. On the other hand, Quality-of-Experience (QoE) is a new concept related to but differs from Quality-of-Service (QoS) perception. QoE is a subjective measure of a customer's experiences with a service focuses on the entire service experience, and is a more holistic evaluation. So far, QoS issues have been focused and mainly addressed in the literature of MSNs.

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“…In [22], a scheduling algorithm is proposed to deal with real-time and non-realtime traffic in a proportional fair manner. More recent works [23][24][25][26][27][28] propose QoE-aware schedulers whose aim is to optimize the overall QoE while ensuring a minimum QoE for all users. All of them decide the exact resources assigned to every single user in real time, which makes them suitable for minimum QoS/QoE assurance.…”

Section: Introductionmentioning

confidence: 99%

Self-tuning of scheduling parameters for balancing the quality of experience among services in LTE

Oliver-Balsalobre

Toril

Luna-Ramírez

et al. 2016

J Wireless Com Network

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Improving the Quality of Experience (QoE) offered to subscribers has become a major issue for mobile network operators. In this paper, a self-tuning algorithm for adjusting parameters in a multi-service packet scheduler of a radio base station based on network statistics is proposed to balance QoE across services in Long Term Evolution (LTE). The aim of the algorithm is to ensure that all users achieve the same average QoE regardless of the type of service. For this purpose, the proposed heuristic algorithm iteratively changes service priority parameters to re-prioritize services so that those services with the lowest QoE increase their priority. Unlike previous approaches, the proposed algorithm takes QoE (and not Quality of Service) into account. Method assessment is carried out in a dynamic system-level LTE simulator. Simulation results in a typical scenario show that tuning service priority parameters can significantly increase the QoE of the worst service without affecting the overall network QoE.

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QoE-Based Cross-Layer Optimization of Wireless Video with Unperceivable Temporal Video Quality Fluctuation

Cited by 56 publications

References 9 publications

Interference Shaping for Improved Quality of Experience for Real-Time Video Streaming

Interference Shaping for Improved Quality of Experience for Real-Time Video Streaming

Quality-of-Experience (QoE) in Emerging Mobile Social Networks

Self-tuning of scheduling parameters for balancing the quality of experience among services in LTE

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