As video traffic continues to dominate the Internet, interest in near-second low-latency streaming has increased. Existing low-latency streaming platforms rely on using tens of seconds of video in the buffer to offer a seamless experience. Striving for near-second latency requires the receiver to make quick decisions regarding the download bitrate and the playback speed. To cope with the challenges, we design a new adaptive bitrate (ABR) scheme, Stallion, for STAndard Low-LAtency vIdeo cONtrol. Stallion uses a sliding window to measure the mean and standard deviation of both the bandwidth and latency. We evaluate Stallion and compare it to the standard DASH DYNAMIC algorithm over a variety of networking conditions. Stallion shows 1.8x increase in bitrate, and 4.3x reduction in the number of stalls.

This paper focuses on COSMOS - Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment. The COSMOS testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. It will enable researchers to explore the technology "sweet spot" of ultra-high bandwidth and ultra-low latency in the most demanding real-world environment. We describe the testbed's architecture, the design and deployment challenges, and the experience gained during the design and pilot deployment. Specifically, we describe COSMOS' computing and network architectures, the critical building blocks, and its programmability at different layers. The building blocks include software-defined radios, 28 GHz millimeter-wave phased array modules, optical transport network, core and edge cloud, and control and management software. We describe COSMOS' deployment phases in a dense urban environment, the research areas that could be studied in the testbed, and specific example experiments. Finally, we discuss our experience with using COSMOS as an educational tool.

As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for RE al-time QU ality of experience metric detection for E ncrypted T raffic— Requet —which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.

The Cloud-Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) platform is a programmable city-scale shared multi-user advanced wireless testbed that is being deployed in West Harlem of New York City [1]. To keep pace with the significantly increased wireless link bandwidth and to effectively integrate the emerging C-RANs, COSMOS is designed to incorporate a fast programmable core network for providing connections across different computing layers. A key feature of COSMOS is its dark fiber based optical x-haul network that enables both highly flexible, user defined network topologies and experimentation directly in the optical physical layer. The optical architecture of COSMOS was presented in [2]. In this abstract, we present the tools and services designed to configure and monitor the performance of optical paths and topologies of the COSMOS testbed. In particular, we present the SDN framework that allows testbed users to implement experiments with application-driven control of optical and data networking functionalities.

As video traffic dominates the Internet, it is important for operators to detect video Quality of Experience (QoE) in order to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a Machine Learning (ML) algorithm to predict QoE metrics, specifically, buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12X, 1.53X, and 3.14X, respectively.

Network slicing will allow 5G network operators to offer a diverse set of services over a shared physical infrastructure. We focus on supporting the operation of the Radio Access Network (RAN) slice broker, which maps slice requirements into allocation of Physical Resource Blocks (PRBs). We first develop a new metric, REVA, based on the number of PRBs available to a single Very Active bearer. REVA is independent of channel conditions and allows easy derivation of an individual wireless link's throughput. In order for the slice broker to efficiently utilize the RAN, there is a need for reliable and short term prediction of resource usage by a slice. To support such prediction, we construct an LTE testbed and develop custom additions to the scheduler. Using data collected from the testbed, we compute REVA and develop a realistic time series prediction model for REVA. Specifically, we present the X-LSTM prediction model, based upon Long Short-Term Memory (LSTM) neural networks. Evaluated with data collected in the testbed, X-LSTM outperforms Autoregressive Integrated Moving Average Model (ARIMA) and LSTM neural networks by up to 31%. X-LSTM also achieves over 91% accuracy in predicting REVA. By using X-LSTM to predict future usage, a slice broker is more adept to provision a slice and reduce over-provisioning and SLA violation costs by more than 10% in comparison to LSTM and ARIMA.

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, the deployment and management of eMBMS systems are challenging, due to the lack of real-time feedback from the user equipments (TIEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting stochastic group instructions to all UEs. These instructions indicate the reporting rates as a function of the observed quality of service (QoS). This simple feedback mechanism collects very limited QoS reports from the TIEs. The reports are used for network optimization, thereby ensuring high QoS to the TIEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead while meeting strict QoS requirements under different TIE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS signal-to-noise ratio experienced by the 0.1th percentile of the TIEs with a root mean square error of 0.05% with only 5 to 10 reports per second regardless of the number of TIEs.

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, the most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the multicast dynamic rate adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the RA response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high-quality video streams, where 90% of the nodes report excellent or very good video quality.

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, deployment and management of eMBMS systems is challenging, due to the lack of realtime feedback from the User Equipment (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs. These instructions indicate the reporting rates as a function of the observed Quality of Service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1% percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5 to 10 reports per second regardless of the number of UEs.

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed approaches do not provide performance guarantees. In this paper, we describe the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe includes a lightweight feedback mechanism that allows monitoring channel quality of a large number of users. This feedback allows the system to dynamically optimize the multicast transmission rate at the AP. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices in different scenarios. We show that AMuSe supports high throughput multicast flows to hundreds of receivers while meeting quality requirements and that it outperforms other systems.

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality.

WiFi networks have been globally deployed and most mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In this paper, we present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe is based on accurate receiver feedback and incurs a small control overhead. In particular, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information about the channel quality to the multicast sender. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. AMuSe does not require any changes to the standards or any modifications to the WiFi devices. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices, both with and without interference sources. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions.

IEEE 802.11-based wireless local area networks, referred to as WiFi, have been globally deployed and the vast majority of mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. We propose AMuSe, a scalable and adaptive interference mitigation solution for WiFi multicast services which is based on accurate receiver feedback and that incurs a small control overhead. Specifically, we develop a scheme for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information, such as channel quality or received packet statistics, to the multicast sender. This feedback information is used by the multicast sender to optimize the multicast service quality, e.g., by dynamically adjusting the transmission bit-rate. Our proposed solution does not require any changes to the standards or any modifications to the WiFi devices. We have implemented the proposed solution in the ORBIT testbed and evaluated its performance in large groups with approximately 250 receivers, both with and without interference sources. Our online experiments demonstrate that our system provides practical multicast services that can accommodate hundreds of receivers.