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The study of first order transition (explosive synchronization) in an ensemble (network) of coupled oscillators has been the topic of paramount interest among the researchers for more than onedecade. Several frameworks have been proposed to induce explosive synchronization in a network and it has been reported that phase frustration in a network usually suppresses first order transition in the presence of pairwise interactions among the oscillators. However, on the contrary, by considering networks of phase frustrated coupled oscillators in the presence of higher order interactions (upto 2-simplexes) we show here under certain conditions, phase frustration can promote explosive synchronization in a network. A reduced order model of the network in the thermodynamic limit is derived using the Ott-Antonsen ansatz to explain this surprising result. Analytical treatment of the reduced order model including bifurcation analysis explains the apparent counter intuitive result quite clearly., Comment: 8 pages, 4 figures

Adaptive Bitrate (ABR) Streaming over the cellular networks has been well studied in the literature; however, existing ABR algorithms primarily focus on improving the end-users' Quality of Experience (QoE) while ignoring the resource consumption aspect of the underlying device. Consequently, proactive attempts to download video data to maintain the user's QoE often impact the battery life of the underlying device unless the download attempts are synchronized with the network's channel condition. In this work, we develop EnDASH-5G -- a wrapper over the popular DASH-based ABR streaming algorithm, which establishes this synchronization by utilizing a network-aware video data download mechanism. EnDASH-5G utilizes a novel throughput prediction mechanism for 5G mmWave networks by upgrading the existing throughput prediction models with a transfer learning-based approach leveraging publicly available 5G datasets. It then exploits deep reinforcement learning to dynamically decide the playback buffer length and the video bitrate using the predicted throughput. This ensures that the data download attempts get synchronized with the underlying network condition, thus saving the device's battery power. From a thorough evaluation of EnDASH-5G, we observe that it achieves a near $30.5\%$ decrease in the maximum energy consumption than the state-of-the-art Pensieve ABR algorithm while performing almost at par in QoE., Comment: 14 pages, 11 figures, journal

Middleboxes such as web proxies, firewalls, etc. are widely deployed in today's network infrastructure. As a result, most protocols need to adapt their behavior to co-exist. One of the most commonly used transport protocols, QUIC, adapts to such middleboxes by falling back to TCP, where they block it. In this paper, we argue that the blind fallback behavior of QUIC, i.e., not distinguishing between failures caused by middleboxes and that caused by network congestion, hugely impacts the performance of QUIC. For this, we focus on YouTube video streaming and conduct a measurement study by utilizing production endpoints of YouTube by enabling TCP and QUIC at a time. In total, we collect over 2600 streaming hours of data over various bandwidth patterns, from 5 different geographical locations and various video genres. To our surprise, we observe that the legacy setup (TCP) either outperforms or performs the same as the QUIC-enabled browser for more than 60% of cases. We see that our observation is consistent across individual QoE parameters, bandwidth patterns, locations, and videos. Next, we conduct a deep-dive analysis to discover the root cause behind such behavior. We find a good correlation (0.3-0.7) between fallback and QoE drop events, i.e., quality drop and re-buffering or stalling. We further perform Granger causal analysis and find that fallback Granger causes either quality drop or stalling for 70% of the QUIC-enabled sessions. We believe our study will help designers revisit the decision to enable fallback in QUIC and distinguish between the packet drops caused by middleboxes and network congestion.

With increasing advancements in technologies for capturing 360{\deg} videos, advances in streaming such videos have become a popular research topic. However, streaming 360{\deg} videos require high bandwidth, thus escalating the need for developing optimized streaming algorithms. Researchers have proposed various methods to tackle the problem, considering the network bandwidth or attempt to predict future viewports in advance. However, most of the existing works either (1) do not consider video contents to predict user viewport, or (2) do not adapt to user preferences dynamically, or (3) require a lot of training data for new videos, thus making them potentially unfit for video streaming purposes. We develop PARIMA, a fast and efficient online viewport prediction model that uses past viewports of users along with the trajectories of prime objects as a representative of video content to predict future viewports. We claim that the head movement of a user majorly depends upon the trajectories of the prime objects in the video. We employ a pyramid-based bitrate allocation scheme and perform a comprehensive evaluation of the performance of PARIMA. In our evaluation, we show that PARIMA outperforms state-of-the-art approaches, improving the Quality of Experience by over 30\% while maintaining a short response time., Comment: 12 pages

Multipath TCP (MPTCP) can exploit multiple available interfaces at the end devices by establishing concurrent multiple connections between source and destination. MPTCP is a drop-in replacement for TCP and this makes it an attractive choice for various applications. In recent times, MPTCP is finding its way into newer devices such as robots and Unmanned Aerial Vehicles (UAVs). However, its usability is often restricted due to unavailability of suitable socket APIs to control its behaviour at the application layer. In this paper, we have introduced several socket APIs to control the sub-flow properties of MPTCP at the application layer. We have proposed a modification in MPTCP kernel data-structure to make the sub-flow priority persistent across sub-flow failures. We have also presented Primary Path only Scheduler (PPoS), a novel sub-flow scheduler, for UAVs and similar applications/devices where it is necessary to segregate data on different links based upon type of data or Quality of Service (QoS) requirements. We have also introduced the socket APIs for providing the fine grained control over the behaviour of PPoS for particular application(s) rather than changing the behaviour system wide. The scheduler and the socket APIs are extensively tested in Mininet based emulation environment as well as on real Raspberry Pi based testbed.