Improving Resilience of Communication in Information Dissemination for Time-Critical Applications

Severe weather impacts life and in this dire condition, people rely on communication, to organize relief and stay in touch with their loved ones. In such situations, cellular network infrastructure\footnote{We refer to cellular network infrastructure as infrastructure for the entirety of this document} might be affected due to power outage, link failures, etc. This urges us to look at Ad-hoc mode of communication, to offload major traffic partially or fully from the infrastructure, depending on the status of it. We look into threefold approach, ranging from the case where the infrastructure is completely unavailable, to where it has been replaced by make shift low capacity mobile cellular base station. First, we look into communication without infrastructure and timely, dissemination of weather alerts specific to geographical areas. We look into the specific case of floods as they affect significant number of people. Due to the nature of the problem we can utilize the properties of Information Centric Networking (ICN) in this context, namely: i) Flexibility and high failure resistance: Any node in the network that has the information can satisfy the query ii) Robust: Only sensor and car need to communicate iii) Fine grained geo-location specific information dissemination. We analyze how message forwarding using ICN on top of Ad hoc network, approach compares to the one based on infrastructure, that is less resilient in the case of disaster. In addition, we compare the performance of different message forwarding strategies in VANETs (Vehicular Adhoc Networks) using ICN. Our results show that ICN strategy outperforms the infrastructure-based approach as it is 100 times faster for 63\% of total messages delivered. Then we look into the case where we have the cellular network infrastructure, but it is being pressured due to rapid increase in volume of network traffic (as seen during a major event) or it has been replaced by low capacity mobile tower. In this case we look at offloading as much traffic as possible from the infrastructure to device-to-device communication. However, the host-oriented model of the TCP/IP-based Internet poses challenges to this communication pattern. A scheme that uses an ICN model to fetch content from nearby peers, increases the resiliency of the network in cases of outages and disasters. We collected content popularity statistics from social media to create a content request pattern and evaluate our approach through the simulation of realistic urban scenarios. Additionally, we analyze the scenario of large crowds in sports venues. Our simulation results show that we can offload traffic from the backhaul network by up to 51.7\%, suggesting an advantageous path to support the surge in traffic while keeping complexity and cost for the network operator at manageable levels. Finally, we look at adaptive bit-rate streaming (ABR) streaming, which has contributed significantly to the reduction of video playout stalling, mainly in highly variable bandwidth conditions. ABR clients continue to suffer from the variation of bit rate qualities over the duration of a streaming session. Similar to stalling, these variations in bit rate quality have a negative impact on the users’ Quality of Experience (QoE). We use a trace from a large-scale CDN to show that such quality changes occur in a significant amount of streaming sessions and investigate an ABR video segment retransmission approach to reduce the number of such quality changes. As the new HTTP/2 standard is becoming increasingly popular, we also see an increase in the usage of HTTP/2 as an alternative protocol for the transmission of web traffic including video streaming. Using various network conditions, we conduct a systematic comparison of existing transport layer approaches for HTTP/2 that is best suited for ABR segment retransmissions. Since it is well known that both protocols provide a series of improvements over HTTP/1.1, we perform experiments both in controlled environments and over transcontinental links in the Internet and find that these benefits also “trickle up” into the application layer when it comes to ABR video streaming where HTTP/2 retransmissions can significantly improve the average quality bitrate while simultaneously minimizing bit rate variations over the duration of a streaming session. Taking inspiration from the first two approaches, we take into account the resiliency of a multi-path approach and further look at a multi-path and multi-stream approach to ABR streaming and demonstrate that losses on one path have very little impact on the other from the same multi-path connection and this increases throughput and resiliency of communication

QOE-AWARE CONTENT DISTRIBUTION SYSTEMS FOR ADAPTIVE BITRATE VIDEO STREAMING

A prodigious increase in video streaming content along with a simultaneous rise in end system capabilities has led to the proliferation of adaptive bit rate video streaming users in the Internet. Today, video streaming services range from Video-on-Demand services like traditional IP TV to more recent technologies such as immersive 3D experiences for live sports events. In order to meet the demands of these services, the multimedia and networking research community continues to strive toward efficiently delivering high quality content across the Internet while also trying to minimize content storage and delivery costs. The introduction of flexible and adaptable technologies such as compute and storage clouds, Network Function Virtualization and Software Defined Networking continue to fuel content provider revenue. Today, content providers such as Google and Facebook build their own Software-Defined WANs to efficiently serve millions of users worldwide, while NetFlix partners with ISPs such as ATT (using OpenConnect) and cloud providers such as Amazon EC2 to serve their content and manage the delivery of several petabytes of high-quality video content for millions of subscribers at a global scale, respectively. In recent years, the unprecedented growth of video traffic in the Internet has seen several innovative systems such as Software Defined Networks and Information Centric Networks as well as inventive protocols such as QUIC, in an effort to keep up with the effects of this remarkable growth. While most existing systems continue to sub-optimally satisfy user requirements, future video streaming systems will require optimal management of storage and bandwidth resources that are several orders of magnitude larger than what is implemented today. Moreover, Quality-of-Experience metrics are becoming increasingly fine-grained in order to accurately quantify diverse content and consumer needs. In this dissertation, we design and investigate innovative adaptive bit rate video streaming systems and analyze the implications of recent technologies on traditional streaming approaches using real-world experimentation methods. We provide useful insights for current and future content distribution network administrators to tackle Quality-of-Experience dilemmas and serve high quality video content to several users at a global scale. In order to show how Quality-of-Experience can benefit from core network architectural modifications, we design and evaluate prototypes for video streaming in Information Centric Networks and Software-Defined Networks. We also present a real-world, in-depth analysis of adaptive bitrate video streaming over protocols such as QUIC and MPQUIC to show how end-to-end protocol innovation can contribute to substantial Quality-of-Experience benefits for adaptive bit rate video streaming systems. We investigate a cross-layer approach based on QUIC and observe that application layer-based information can be successfully used to determine transport layer parameters for ABR streaming applications

Real-Time Neural Video Recovery and Enhancement on Mobile Devices

As mobile devices become increasingly popular for video streaming, it's crucial to optimize the streaming experience for these devices. Although deep learning-based video enhancement techniques are gaining attention, most of them cannot support real-time enhancement on mobile devices. Additionally, many of these techniques are focused solely on super-resolution and cannot handle partial or complete loss or corruption of video frames, which is common on the Internet and wireless networks. To overcome these challenges, we present a novel approach in this paper. Our approach consists of (i) a novel video frame recovery scheme, (ii) a new super-resolution algorithm, and (iii) a receiver enhancement-aware video bit rate adaptation algorithm. We have implemented our approach on an iPhone 12, and it can support 30 frames per second (FPS). We have evaluated our approach in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows that our approach enables real-time enhancement and results in a significant increase in video QoE (Quality of Experience) of 24\% - 82\% in our video streaming system

An Experimental Study of the Server-based Unfairness Solutions for the Cross-Protocol Scenario of Adaptive Streaming over HTTP/3 and HTTP/2

منذ إدخال HTTP / 3 ، ركز البحث على تقييم تأثيره على البث التكيفي الحالي عبر HTTP (HAS). من بين هذه الأبحاث ، نظرًا لبروتوكولات النقل غير ذات الصلة ، حظي الظلم عبر البروتوكولات بين HAS عبر HTTP / 3 (HAS / 3) و HAS عبر HTTP / 2 (HAS / 2) باهتمام كبير. لقد وجد أن عملاء HAS / 3 يميلون إلى طلب معدلات بت أعلى من عملاء HAS / 2 لأن النقل QUIC يحصل على عرض نطاق ترددي أعلى لعملائه HAS / 3 من TCP لعملائه HAS / 2. نظرًا لأن المشكلة تنشأ من طبقة النقل ، فمن المحتمل أن حلول الظلم المستندة إلى الخادم يمكن أن تساعد العملاء في التغلب على مثل هذه المشكلة. لذلك ، في هذه الورقة ، تم إجراء دراسة تجريبية لحلول الظلم القائمة على الخادم لسيناريو البروتوكول المتقاطع لـ HAS / 3 و HAS / 2. تظهر النتائج أنه على الرغم من فشل حل توجيه معدل البت في مساعدة العملاء على تحقيق العدالة ، فإن حل تخصيص النطاق الترددي يوفر أداءً فائقًا.Since the introduction of the HTTP/3, research has focused on evaluating its influences on the existing adaptive streaming over HTTP (HAS). Among these research, due to irrelevant transport protocols, the cross-protocol unfairness between the HAS over HTTP/3 (HAS/3) and HAS over HTTP/2 (HAS/2) has caught considerable attention. It has been found that the HAS/3 clients tend to request higher bitrates than the HAS/2 clients because the transport QUIC obtains higher bandwidth for its HAS/3 clients than the TCP for its HAS/2 clients. As the problem originates from the transport layer, it is likely that the server-based unfairness solutions can help the clients overcome such a problem. Therefore, in this paper, an experimental study of the server-based unfairness solutions for the cross-protocol scenario of the HAS/3 and HAS/2 is conducted. The results show that, while the bitrate guidance solution fails to help the clients achieve fairness, the bandwidth allocation solution provides superior performance

Video Adaptation for High-Quality Content Delivery

Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes (rebuffers) and enhancing the quality of the video. A bitrate that is too high leads to frequent rebuffering, while a bitrate that is too low leads to poor video quality. In this dissertation we propose video-adaptation algorithms to deliver content and maximize the viewer\u27s quality of experience (QoE). Video providers partition videos into short segments and encode each segment at multiple bitrates. The video player adaptively chooses the bitrate of each segment to download, possibly choosing different bitrates for successive segments. We formulate bitrate adaptation as a utility-maximization problem, and design algorithms to provide provably near-optimal time-average utility. Real-world systems are generally too complex to be fully represented in a theoretical model and thus present a new set of challenges. We design algorithms that deliver video on production systems, maintaining the strengths of the theoretical algorithms while also tackling challenges faced in production. Our algorithms are now part of the official DASH reference player dash.js and are being used by video providers in production environments. Most online video is streamed via HTTP over TCP. TCP provides reliable delivery at the expense of additional latency incurred when retransmitting lost packets and head-of-line blocking. Using QUIC allows the video player to tolerate some packet loss without incurring the performance penalties. We design and implement algorithms that exploit this added flexibility to provide higher overall QoE by reducing latency and rebuffering while allowing some packet loss. Recently virtual reality content is increasing in popularity, and delivering 360° video comes with new challenges and opportunities. The viewing space is often partitioned in tiles, and a viewer using a head-mounted display only sees a subset of the tiles at any time. We develop an open source simulation environment for fast and reproducible testing of 360° algorithms. We develop adaptation algorithms that provide high QoE by allocating more bandwidth resources to deliver the tiles that the viewer is more likely to see, while ensuring that the video player reacts in a timely manner when the viewer changes their head pose

Lightweight, General Inference of Streaming Video Quality from Encrypted Traffic

Accurately monitoring application performance is becoming more important for Internet Service Providers (ISPs), as users increasingly expect their networks to consistently deliver acceptable application quality. At the same time, the rise of end-to-end encryption makes it difficult for network operators to determine video stream quality-including metrics such as startup delay, resolution, rebuffering, and resolution changes-directly from the traffic stream. This paper develops general methods to infer streaming video quality metrics from encrypted traffic using lightweight features. Our evaluation shows that our models are not only as accurate as previous approaches , but they also generalize across multiple popular video services, including Netflix, YouTube, Amazon Instant Video, and Twitch. The ability of our models to rely on lightweight features points to promising future possibilities for implementing such models at a variety of network locations along the end-to-end network path, from the edge to the core

Video QoE Estimation using Network Measurement Data

More than even before, last-mile Internet Service Providers (ISPs) need to efficiently provision and manage their networks to meet the growing demand for Internet video (expected to be 82% of the global IP traffic in 2022). This network optimization requires ISPs to have an in-depth understanding of end-user video Quality of Experience (QoE). Understanding video QoE, however, is challenging for ISPs as they generally do not have access to applications at end user devices to observe key objective metrics impacting QoE. Instead, they have to rely on measurement of network traffic to estimate objective QoE metrics and use it for troubleshooting QoE issues. However, this can be challenging for HTTP-based Adaptive Streaming (HAS) video, the de facto standard for streaming over the Internet, because of the complex relationship between the network observable metrics and the video QoE metrics. This largely results from its robustness to short-term variations in the underlying network conditions due to the use of the video buffer and bitrate adaptation. In this thesis, we develop approaches that use network measurement to infer video QoE. In developing inference approaches, we provide a toolbox of techniques suitable for a diversity of streaming contexts as well as different types of network measurement data. We first develop two approaches for QoE estimation that model video sessions based on the network traffic dynamics of the HAS protocol under two different streaming contexts. Our first approach, MIMIC, estimates unencrypted video QoE using HTTP logs. We do a large-scale validation of MIMIC using ground truth QoE metrics from a popular video streaming service. We also deploy MIMIC in a real-world cellular network and demonstrate some preliminary use cases of QoE estimation for ISPs. Our second approach is called eMIMIC that estimates QoE metrics for encrypted video using packet-level traces. We evaluate eMIMIC using an automated experimental framework under realistic network conditions and show that it outperforms state-of-the-art QoE estimation approaches. Finally, we develop an approach to address the scalability challenges of QoE inference. We leverage machine learning to infer QoE from coarse-granular but light-weight network data in the form of Transport Layer Security (TLS) transactions. We analyze the scalability and accuracy trade-off in using such data for inference. Our evaluation shows that that the TLS transaction data can be used for detecting video performance issues with a reasonable accuracy and significantly lower computation overhead as compared to packet-level traces.Ph.D