Content-aware packet scheduling for multiuser scalable video delivery over wireless networks

Maani, Ehsan; Pahalawatta, Peshala V.; Berry, Randall A.; Katsaggelos, Aggelos K.

doi:10.1117/12.829402

Cited by 4 publications

(7 citation statements)

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“…A content-aware downlink packet scheduling scheme for multi-user scalable video delivery over wireless networks is proposed in [48]. The scheduler uses a gradient-based scheduling framework, as elaborated earlier in [37], along with scalable video coding schemes.…”

Section: A Quality Driven Schedulingmentioning

confidence: 99%

Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Nasralla

Khan

Martini

2018

Computer Communications

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We present in this paper a comprehensive review and comparison of recent downlink scheduling approaches for video streaming traffic over the Orthogonal Frequency Division Multiple Access (OFDMA) based Long-Term Evolution (LTE) wireless technology. Focusing on content-aware downlink scheduling approaches, we provide an extensive literature review, a taxonomy for content-aware and content-unaware downlink schedulers, and tables that summarize the key approaches and common parameters among the schedulers. In addition, we analyze and compare via simulation the performance of some of the most relevant scheduling rules. Our main goal is to compare and analyze different classes of scheduling strategies in terms of network centric performance metrics as well as user centric metrics. Quality of Service (QoS) evaluation involves the evaluation of network performance parameters, e.g., packet loss rate, average system throughput and end-to-end packet delay. On the other hand, Quality of Experience (QoE) reflects the user's experience and satisfaction in terms of Mean Opinion Score (MOS). According to simulation results, proxy based QoE aware scheduling strategies perform best in terms of number of satisfied users and should be used in an LTE downlink to offer high quality video streaming services.

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Section: A Quality Driven Schedulingmentioning

confidence: 99%

Content-aware downlink scheduling for LTE wireless systems: A survey and performance comparison of key approaches

Nasralla

Khan

Martini

2018

Computer Communications

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“…This mode of operation requires significantly less side information than the content aware schedulers considered in the literature [13] because the scheduler only needs to care about which layer the individual packets belong to. In the following we describe the test video sequences and the distortion model of scalable video coding used in the paper, following which we describe the utility functions of the individual users.…”

Section: System Setupmentioning

confidence: 99%

“…Note that defining the utility as a function of the normalized channel distortion is common in the literature. For example, in [13] the utility gradient of a video packet is defined as the channel distortion caused by the loss of that packet normalized by the packet's size.…”

Section: User Utility Modelmentioning

confidence: 99%

“…Threshold SNRs required to receive data at the respective data rates with P b ≤ 10 -6 are provided in the fifth column. They are calculated based on an empirical model described in [13]:…”

Section: Channel Modelmentioning

confidence: 99%

“…Unicast scheduling of scalable video streams has been recently addressed in [12,13]. With scalable video coding, the video stream is encoded into a base layer and several enhancement layers.…”

Section: Introductionmentioning

confidence: 99%

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Multicast scheduling for scalable video streaming in wireless networks

Vukadinović

Dán

2010

Proceedings of the First Annual ACM SIGMM Conference on Multimedia Systems

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We consider how relatively simple extensions of popular channelaware schedulers can be used to multicast scalable video streams in high speed radio access networks. To support the evaluation, we first describe a model of the channel distortion of scalable video coding and validate it using eight commonly used test sequences. We use the distortion model in a detailed simulation setup to compare the performance of six schedulers, among them the Max-Sum and Max-Prod schedulers, which aim to maximize the sum and the product of streaming utilities, respectively. We investigate how the traffic load, user mobility, layering structure, and users' aversion of fluctuating distortion influence the streaming performance. Our results show that the Max-Sum scheduler performs better than other considered schemes in almost all scenarios. With the Max-Sum scheduler, the gain of scalable video coding compared to non-scalable coding is substantial, even when users do not tolerate frequent changes in video quality.

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