Availability-Aware Multimedia Scheduling in Heterogeneous Wireless Networks

Zhou, Liang; Wang, Haohong; Lian, Shiguo; Zhang, Yan; Vasilakos, Athanasios V.; Jing, Weiping

doi:10.1109/tvt.2011.2104420

Cited by 23 publications

(17 citation statements)

References 27 publications

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“…It also optimizes bit rate switching [8]. iProxy is a mobile video-centric proxy cache that offers better hit rates and streaming quality as compared to state- [7,9] Playback smoothness Medium Very low [8, 11-14, 28, 31-34, 36-40] Classical objective quality metric Very low Low [4, 15-21, 41, 117] Subjective quality metric Very low Medium [26][27][28][29][30][31][32][33] Subjective quality metric for scalable video coding Medium Medium [43][44][45][46][47][48][49][50] Subjective QoE metric in lossy environment High High [22,42,[59][60][61][62][63][64][65][66][67][68][69][70][71][72][73] Video quality metric for advanced video coding High High [74][75][76][77][78][79][80][81][82]…”

Section: Playback Smoothness As Quality Metricmentioning

confidence: 99%

“…In the video streaming application, it is desired to have a simple method, such as creating a mapping function between the MOS and the easy-to-calculated objective quality metric, to optimize the system performance. Zhou et al [43] presented a general media distortion model taking into account not only traditional QoS metrics (i.e., data loss and delay), but also resource availability. Leveraging this model, a utility-based rate allocation scheme (UBRA) was developed for multimedia over a heterogeneous wireless network.…”

Section: Subjective Quality Mapped From Classical Objective Metricmentioning

confidence: 99%

See 1 more Smart Citation

QoE in video streaming over wireless networks: perspectives and research challenges

Bai

et al. 2015

Wireless Netw

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The deployment of 3G/LTE networks and advancements in smart mobile devices had led to high demand for multimedia streaming over wireless network. The rapid increasing demand for multimedia content poses challenges for all parties in a multimedia streaming system, namely, content providers, wireless network service providers, and smart device makers. Content providers and mobile network service providers are both striving to improve their streaming services while utilizing advancing technologies. Smart device makers endeavor to improve processing power and displays for better viewing experience. Ultimately, the common goal shared by content providers, network service providers, and smart device manufactures is to improve the QoE for users. QoE is both an objective and a subjective metric measuring the streaming quality experience by end users. It may be measured by streaming bitrate, playback smoothness, video quality metrics like Peak to Signal Noise Ratio, and other user satisfaction factors. There have been efforts made to improve the streaming experiences in all these aspects. In this paper, we conducted a survey on existing literatures on QoE of video streaming to gain a deeper and more complete understanding of QoE quality metrics. The goal is to inspire new research directions in defining better QoE and improving QoE in existing and new streaming services such as adaptive streaming and 3D video streaming.

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Section: Playback Smoothness As Quality Metricmentioning

confidence: 99%

Section: Subjective Quality Mapped From Classical Objective Metricmentioning

confidence: 99%

QoE in video streaming over wireless networks: perspectives and research challenges

Bai

et al. 2015

Wireless Netw

Self Cite

View full text Add to dashboard Cite

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“…In addition, research efforts have been focused in rate-allocation and scheduling for delay sensitive multimedia applications over heterogeneous and error-prone networks [60,61]. However, provisioning of end-to-end QoS over heterogeneous networks is very complex.…”

Section: Literature Reviewmentioning

confidence: 99%

QoS-aware scalable video streaming using data distribution service

Almadani

Al-Saeedi

Al-Roubaiey

2015

Multimed Tools Appl

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Enabling Real-Time video streaming over wireless networks is a challenging task due to the time-varying channel conditions and the limited network resources. The instability of wireless networks leads to problems such as limited and time-varying bandwidth, and unexpected traffic congestion when transmitting a burst of video streams. In Real-Time video streaming, each frame must be delivered and decoded by its playback time. Recently, layer coding (LC) has enabled Real-Time and scalable video streaming to clients of heterogeneous capabilities by dropping upper enhancement layers without the need of re-encoding and with much less bit rate. However, layer coding still facing unfair layer protection problem in which packets from the base or lower layers might be dropped while there is a chance to drop packets from the upper enhancement layers. Losing packets from the base layer can significantly affect the delivered video quality and sometimes lead to an interruption especially in error-prone networks as wireless networks. Architectural solutions at the middleware level introduce higher flexibility, more efficiency in development time and more QoS control. This work investigates the behavior of video streaming over Real-Time publish-subscribe based middleware. We propose and develop an unequal layer protection mechanism for Real-Time video streaming based on the Data Distribution Service (DDS) middleware. A combination of video quality of service (QoS) is proposed to adapt the video transmission to the time-varying network changes. The performance of Real-Time video streaming is measured over IEEE 802.11 g WLAN network. The results show a graceful degradation of video quality while maintaining a robust video streaming free of visible error or interruptions. This is due to the intentionally dropping of some enhancement video stream layers in order to protect lower layers, and therefore maintain a continuous video flow of acceptable quality. Numerical results indicate that the proposed scheme is able to achieve higher throughput and improve the average received video quality especially in a large number of video subscribers.

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“…Several studies, e.g., [44,45], and [20], addressed changing media representation depending on the available resources as users roam in the network. However, these studies do not consider other sources of system diversity such as cost variability.…”

Section: Introductionmentioning

confidence: 99%

A framework for optimal cost media streaming in three-tier wireless networks

Al-Abbasi

Zahran

2016

Wireless Netw

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Heterogeneous networking is envisioned as a key solution for accommodating the traffic surge resulting from resource demanding applications such as video streaming. In this paper, the diversity in cost, coverage, and resource availability in heterogeneous systems is exploited to minimize the streaming session cost in three-tier integrated systems. A sub-optimal streaming decision engine is developed to overcome the complexity of the original problem whose solution contradicts with both the limited processing capabilities of end-user equipment and short handoff delay requirements. Our results show that the developed framework achieves significant monetary cost savings in comparison to typical greedy streaming behavior. Additionally, our solution can be easily tuned to compromise the tradeoff between monetary, signaling and quality cost components.

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Availability-Aware Multimedia Scheduling in Heterogeneous Wireless Networks

Cited by 23 publications

References 27 publications

QoE in video streaming over wireless networks: perspectives and research challenges

QoE in video streaming over wireless networks: perspectives and research challenges

QoS-aware scalable video streaming using data distribution service

A framework for optimal cost media streaming in three-tier wireless networks

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