Characterizing fairness for 3G wireless networks

Aggarwal, Vaneet; Jana, Rittwik; Pang, Jong-Shi; Ramakrishnan, K. K.; Shankaranarayanan, N.K.

doi:10.1109/lanman.2011.6076940

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…RNCs manage control signaling such as Radio Access Bearer (RAB) assignments, transmission scheduling, and handovers. Each UE negotiates allocation of radio resources with the RAN based on a wide range of factors, such as available radio resources and signal strength [6].…”

Section: Cellular Network Backgroundmentioning

confidence: 99%

Understanding the impact of network dynamics on mobile video user engagement

Shafiq

Erman

et al. 2014

SIGMETRICS Perform. Eval. Rev.

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Mobile network operators have a significant interest in the performance of streaming video on their networks because network dynamics directly influence the Quality of Experience (QoE). However, unlike video service providers, network operators are not privy to the client-or server-side logs typically used to measure key video performance metrics, such as user engagement. To address this limitation, this paper presents the first large-scale study characterizing the impact of cellular network performance on mobile video user engagement from the perspective of a network operator. Our study on a month-long anonymized data set from a major cellular network makes two main contributions. First, we quantify the effect that 31 different network factors have on user behavior in mobile video. Our results provide network operators direct guidance on how to improve user engagement -for example, improving mean signal-to-interference ratio by 1 dB reduces the likelihood of video abandonment by 2%. Second, we model the complex relationships between these factors and video abandonment, enabling operators to monitor mobile video user engagement in real-time. Our model can predict whether a user completely downloads a video with more than 87% accuracy by observing only the initial 10 seconds of video streaming sessions. Moreover, our model achieves significantly better accuracy than prior models that require client-or server-side logs, yet we only use standard radio network statistics and/or TCP/IP headers available to network operators.

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Section: Cellular Network Backgroundmentioning

confidence: 99%

Understanding the impact of network dynamics on mobile video user engagement

Shafiq

Erman

et al. 2014

SIGMETRICS Perform. Eval. Rev.

Self Cite

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“…It has been widely assumed that TCP can provide fair performance for multiple flows. However, measurement studies over a 3G network [9] has shown that the fairness among multiple competing TCP flows fluctuates significantly when the lasthop cellular link is the bottleneck, i.e., TCP can achieve rather unfair throughput (more than 5 times difference) in cellular networks. The major cause behind this phenomenon is the poor interaction between TCP congestion control and proportional fair (PF) scheduling in cellular networks.…”

Section: A Issues Of Has Over Cellularmentioning

confidence: 99%

“…Thus the instable and unfair playback will be inevitably introduced. More unfortunately, measurement studies [9] have shown that the prevalent assumption of TCP fairness may not be true in cellular networks. This indicates that multiple clients would not achieve fair performance even though they somehow learn the channel information and request the true bandwidth share.…”

Section: Introductionmentioning

confidence: 99%

Prius: Hybrid Edge Cloud and Client Adaptation for HTTP Adaptive Streaming in Cellular Networks

Yan

Xue

Chen

2017

IEEE Trans. Circuits Syst. Video Technol.

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In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing application-layer and radio access networks (RAN) information in real time. Coupled with powerful computation support, an edge cloud assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks.To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics as well as applicationlayer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast moving environment. Furthermore, Prius adaptation algorithm achieves a near-optimal performance that outperforms exiting strategies.

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“…We also consider network related metrics such as resource utilisation and fairness, which is estimated as [17] …”

Section: A Dash Encoding Setupmentioning

confidence: 99%