Many network applications, eg, industrial control, demand ultra-low latency (ULL) However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds The IEEE 8021 time sensitive networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF deterministic networking (DetNet) standards seek to provide the complementary network layer ULL support This paper provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management Throughout, we identify the pitfalls and limitations of the existing standards and research studies This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Many network applications, e.g., industrial control, demand Ultra-Low Latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive Networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF Deterministic Networking (DetNet) standards seek to provide the complementary network layer ULL support. This article provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations.

pdf/ultra-low-latency-ull-networks-the-ieee-tsn-and-ietf-detnet-lp6ztww5sf.pdf

A large fraction of video content providers have adopted adaptive bitrate streaming over HTTP. The client player typically runs an adaptive bitrate (ABR) algorithm to decide upon the most optimal quality for the next few seconds of video playback. State-of-the-art ABR algorithms attempt to achieve an optimal trade-off among the competing objectives of high bitrate, less rebuffering, and high smoothness, in the face of unpredictable bandwidth variability. However, optimal bandwidth utilization does not necessarily ensure high quality of experience (QoE). Different users have different content preferences even within the same video, due to differences in team loyalties (in sport), character preferences (in movies and soaps), and so on. In this work, we present HotDASH, a system which enables opportune prefetching of user-preferred temporal video segments (called hotspots). HotDASH implements a prefetch module in the open source DASH player dash.js, which is powered by an optimal prefetch and bitrate decision engine. The decision engine is designed as a cascaded reinforcement learning (RL) model, implemented using a state-of-the-art actor-critic RL algorithm over a neural network. We train the neural network using trace-driven simulations over a large variety of bandwidth conditions. HotDASH outperforms all baseline algorithms, with a 16.2% QoE improvement over the best-performing baseline, and achieves 14.31% better average bitrate due to its ability to prefetch opportunistically.

HotDASH: Hotspot Aware Adaptive Video Streaming Using Deep Reinforcement Learning

Video streaming today accounts for up to 55\% of mobile traffic. In this paper, we explore streaming videos encoded using Scalable Video Coding scheme (SVC) over highly variable bandwidth conditions such as cellular networks. SVC's unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop Layered Bin Packing (LBP) Adaptation Algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed including handling bandwidth prediction errors, and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared to the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today's mobile devices.

LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming

Video streaming today accounts for up to 55% of mobile traffic. In this paper, we explore streaming videos encoded using scalable video coding (SVC) scheme over highly variable bandwidth conditions, such as cellular networks. SVC’s unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel quality-of-experience metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop layered bin packing (LBP) adaptation algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed, including handling bandwidth prediction errors and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared with the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today’s mobile devices.

Real-time communication (RTC) applications like video conferencing or cloud gaming require consistent low latency to provide a seamless interactive experience. However, wireless networks including WiFi and cellular, albeit providing a satisfactory median latency, drastically degrade at the tail due to frequent and substantial wireless bandwidth fluctuations. We observe that the control loop for the sending rate of RTC applications is inflated when congestion happens at the wireless access point (AP), resulting in untimely rate adaption to wireless dynamics. Existing solutions, however, suffer from the inflated control loop and fail to quickly adapt to bandwidth fluctuations. In this paper, we propose Zhuge, a pure wireless AP based solution that reduces the control loop of RTC applications by separating congestion feedback from congested queues. We design a Fortune Teller to precisely estimate per-packet wireless latency upon its arrival at the wireless AP. To make Zhuge deployable at scale, we also design a Feedback Updater that translates the estimated latency to comprehensible feedback messages for various protocols and immediately delivers them back to senders for rate adaption. Trace-driven and real-world evaluation shows that Zhuge reduces the ratio of large tail latency and RTC performance degradation by 17% to 95%.

/pdf/achieving-consistent-low-latency-for-wireless-real-time-15ge6dok.pdf

Achieving consistent low latency for wireless real-time communications with the shortest control loop

Many commercial video players rely on bitrate adaptation algorithm to adapt video bitrate to dynamic network condition. To achieve a high quality of experience, bitrate adaptation algorithm is required to strike a balance between response agility and video quality stability. Existing online algorithms select bitrates according to instantaneous throughput and buffer occupancy, achieving an agile reaction to changes but inducing video quality fluctuations due to the high dynamic of reference signals. In this paper, the idea of multi-step prediction is proposed to guide a better tradeoff, and the bitrate selection is formulated as a predictive control problem. With it, a generalized predictive control based approach is developed to calculate the optimal bitrate by minimizing the cost function over a moving look-ahead horizon. Finally, the proposed algorithm is implemented on a reference video player with performance evaluations conducted using realistic bandwidth traces. Experimental results show that the multi-step predictive control adaptation algorithm can achieve zero rebuffer event and 63.3% of reduction in bitrate switch.

Towards Forward-looking Online Bitrate Adaptation for DASH

Owing to Ethernet's low cost, high bandwidth and architecture openness, much attention has been paid to develop converged Ethernet to support both time-critical services and conventional communication services on a unified network infrastructure. The greatest challenge here is providing low and deterministic latency for time-critical packets. Recently, the IEEE time sensitive networking task group is launched to address it. However, their framework is complex and unsuitable for commodity switch architecture. In this paper, we propose a concise and efficient converged real-time Ethernet framework called XpressEth, which leverages Dual Preemption mechanism to minimize the delay of time-critical packets, and employs a lightweight Slot Assignment Scheduler to minimize the conflicts among time-critical packets at sources. XpressEth cuts off great burden from both forwarding and scheduling. The simulation results verify that XpressEth can provide ultra-low and deterministic latency for time-critical packets (1.024µ s per hop and zero jitter in 1Gbps network), which is 13× better than time sensitive networking solution, and the side-effect on conventional communication traffic is negligible.

XpressEth: Concise and efficient converged real-time Ethernet

Today, live streaming is gaining a rapid growth in use, which refers to streaming the media content recorded and broadcast in real time. In live streaming, latency is of utmost importance since smaller latency means higher user engagement. HTTP adaptive streaming (HAS) is now the most popular live streaming technology, where the video client sends HTTP requests to server to download video segments. The bitrate adaptation (ABR) algorithm inside the client determines bitrate level for every segment. It is of great help for ABR algorithm to quantify the influence of different HAS factors on streaming performance. However, existing work mainly focuses on video on demand (VoD) streaming rather than live streaming. In this paper, we theoretically analyze live streaming performance. We first establish a queuing model to describe playout buffer evolution. Based on the model, we respectively characterize rebuffering probability, rebuffering count and streaming latency, and analyze the effects of chunk arrival rate, arrival interval fluctuation, startup threshold and video skipping on them. From analysis results, we propose insights and recommendations for bitrate adaptation in live streaming and design a simple heuristic ABR algorithm leveraging them. Extensive simulations verify the insights as well as effectiveness of the designed algorithm.

Modeling and Analyzing Live Streaming Performance

More and more commercial video players use bitrate adaptation to adjust video quality according to varying network conditions. Optimization-based approaches are widely used for bitrate adaptation in Dynamic Adaptive Streaming over HTTP (DASH). Essentially, the optimization problem is solved based on the prediction of buffer dynamics. However, stochastic chunk size deviates observably the buffer occupancy from the expected value, making the evolution hard to predict. In order to get rid of this effect and improve the prediction accuracy for buffer occupancy, we propose an algorithm based on markov decision process with incorporating chunk size information so that only the network capacity variation need to be considered in the decision-making process. Experiment results show that our solution can effectively eliminate performance oscillation induced by variable chunk size and achieve a good QoE.

Improving Optimization-Based Rate Adaptation in DASH System

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