Enforcing Bitrate-Stability for Adaptive Streaming Traffic in Cellular Networks

2019 IEEE Wireless Communications and Networking Conference (WCNC)

Acharya

Poojary

et al. 2019

Self Cite

In this paper, we develop an analytical framework to compute the Quality-of-Experience (QoE) metrics of video streaming in wireless networks. Our framework takes into account the system dynamics that arises due to the arrival and departure of flows. We also consider the possibility of users abandoning the system on account of poor QoE. Considering the coexistence of multiple services such as video streaming and elastic flows, we use a Markov chain based analysis to compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and bitrate switching. Our simulation results validate the accuracy of our model and describe the impact of scheduler at eNB on the QoE metrics.

Section: B Contribution and Organisationmentioning

confidence: 99%

Section: B Lte Enb Schedulermentioning

confidence: 99%

See 1 more Smart Citation

Analysis of QoE for Adaptive Video Streaming over Wireless Networks with User Abandonment Behavior

2019 IEEE Wireless Communications and Networking Conference (WCNC)

Acharya

Poojary

et al. 2019

Self Cite

“…The simulation results have a variance about the mean which is represented by an error bar. Note: The scheduling policy used in this section is a modification of the D-VIEWS scheduler proposed in [15]. The modification version uses the average throughput of the PF scheduler itself as the target when the average throughput is less than ℓ 1 (the lowest video bitrate).…”

Section: Simulation and Validationmentioning

confidence: 99%

Analysis of QoE for adaptive video streaming over wireless networks

Poojary

2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

Altman

et al. 2018

Self Cite

“…Furthermore, differentiating regular and adaptive video sources is becoming difficult, because more-and-more video content providers are adopting strong encryption, which prevents the usage of network monitoring techniques such as deep packet inspection [9]. In [10], the authors proposed a new scheduler called D-VIEWS, to enforce bitrate stability for each DASH flow in the networks. D-VIEWS performs well in static scenarios but in dynamic scenarios the scheduler tends to generate quality switches due to a reset mechanism employed to handle variations in resources utilization due to user arrival/departure and mobility.…”

Section: Introductionmentioning

confidence: 99%

Dynamic DASH Aware Scheduling in Cellular Networks

2019 IEEE Wireless Communications and Networking Conference (WCNC)

Sunny

Zhao³

et al. 2019

Self Cite

Dynamic Adaptive Streaming over HTTP (DASH) has become the standard choice for live events and on-demand video services. In fact, by performing bitrate adaptation at the client side, DASH operates to deliver the highest possible Quality of Experience (QoE) under given network conditions. In cellular networks, in particular, video streaming services are affected by mobility and cell load variation. In this context, DASH video clients continually adapt the streaming quality to cope with channel variability. However, since they operate in a greedy manner, adaptive video clients can overload cellular network resources, degrading the QoE of other users and suffer persistent bitrate oscillations. In this paper, we tackle this problem using a new eNB scheduler, named Shadow-Enforcer, which ensures minimal number of quality switches as well as efficient and fair utilization of network resources. Our scheduler works well under dynamic scenarios and mobility, and requires minimal information, i.e., just the set of video bitrates supported by DASH video clients. It consists of the cascade of a virtual scheduler, Shadow, and the actual scheduler, Enforcer, piloted by the virtual one. Extensive simulations demonstrate the efficiency, fairness and the smooth response to channel variations of the proposed solution.