Video Streaming Applications Research Articles

Inferring Streaming Video Quality from Encrypted Traffic

Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so.We develop models that infer quality metrics (i.e., startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (e.g., due to aggregation). Second, we develop a single composite model that works for a range of different services (i.e., Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (e.g., the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed" and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.

Direct and Semi-Direct Frame Partitioning Mapping for Computation and Energy-Aware HEVC Transrating

The most common operation to maintain various versions of the same video sequence under different bitrates in live and streaming video applications is video transrating. The typical transrating operation is comprised of a decoding and a complete encoding step in sequence, which results in long processing times and high energy consumption. This work proposes two early-termination schemes based on frame partitioning inheritance between decoding and encoding to accelerate the complex quadtree structure decisions performed during HEVC transrating. The proposed Direct Mapping scheme achieved time saving results of 48.5%, with a compression efficiency loss of 0.818%, on average. The Semi-Direct Mapping reaches 40.2% in time savings at the cost of an average compression efficiency loss of 0.724% in comparison to the original transcoder. Energy consumption was reduced in 50.4%, on average, considering the Direct Mapping approach.

A PDE-based approach for the evaluation of probability of starvation in video streaming

Characterizing the dynamics of wireless channels is of crucial importance to the performance of application requiring quality of experience (QoE) guarantees like wireless-based time-sensitive video streaming applications. Therefore, in this study, we employ a novel approach to model the distribution of the total amount of data that can be transmitted over a wireless channel that is modeled by a multi-state Markov chain with an arbitrary number of states while permitting transitions to non-adjacent states. This is done by deriving a system of partial differential equations (PDEs) that characterizes the channel behavior. Then, the proposed system of PDEs is used to obtain the distribution of the total amount of data that can be transmitted through the wireless channel and evaluate the probability of starvation at the playback buffer in video streaming applications. The solution is presented analytically for a 2-state wireless channel with alternating good and bad intervals, and numerically for the general case of an n state channel. Moreover, the resulting distributions are verified for their accuracy by comparison with the results obtained using Monte-Carlo simulations. Finally, the cumulative distribution function (CDF) of the distribution is then used to evaluate the probability of starvation, based on which a rate control algorithm is proposed.

Reliable Video Multicasting over WLANs

As is known reliable multicast transmission is not addressed in WLANs. Multicast packets are sent over wireless channels without using medium access control (MAC) layer automatic repeat request (ARQ) mechanism. Therefore, packet error rates will be very high. This paper investigates reliable video multicasting, which became a very popular application, over WLANs. To this end, application layer forward error correction (AL-FEC) codes based on Raptor Q can be used as a means to provide reliable delivery of the multicast video encoded with the H.264/Advanced video coding (AVC) codec. Since video streaming applications have very stringent Quality of Service requirements (low delay and error free transmission of packets), the Raptor Q and the H.264/AVC parameters have to be determined depending on the QoS of the video. Therefore, in this work an advanced cross-layer simulator was developed to analyse the performance of the end-to-end system. Simulation results show that using AL-FEC significantly improve the received video quality, i.e., the mean peak signal to noise ratio (PSNR) is improved over 34 dB. Further that it is shown that frequently inserting intra-frame results in poor video quality. Therefore, it is suggested to send intra frame at longer intervals.

Traffic Offloading in Multicast Device-to-Device Cellular Networks: A Combinatorial Auction-Based Matching Algorithm.

In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and audio streaming applications. Therefore, there is need for smart spectrum management policies. In this paper, we consider a fractional frequency reuse (FFR) technique which divides the whole spectrum into multiple sections and allows reusing of spectrum resources between the conventional cellular users and multicast D2D users in a non-orthogonal scenario. Since conventional cellular users and multicast D2D users shared same resources simultaneously, they generate severe data traffic and high communication overhead. To overcome these issues, in this paper we propose Lagrange relaxation technique to solve the non-convex problem and combinatorial auction-based matching algorithm to select the most desirable resource reuse partners by fulfilling the quality of service (QoS) requirements for both the conventional cellular users and multicast D2D users. Then, we formulate an optimization problem to maximize the overall system performance with least computational complexity. We demonstrate that our method can exploit a higher data rate, spectrum efficiency, traffic offload rate, coverage probability, and lower computational complexity.

Segment‐aware dynamic routing for DASH flows over software‐defined networks

SummaryMost of the video streaming applications running over the Internet send video data over HTTP and provide an architecture for video clients to adapt video quality during streaming. In HTTP adaptive streaming, a raw video is encoded at various qualities, each encoded video file is divided into small segments, and the clients may change the segment quality by sending requests for segments having different qualities over time. MPEG has standardized dynamic adaptive streaming over HTTP (MPEG‐DASH) due to this tendency. In this work, we focus on DASH over software‐defined networks (SDN), and we dynamically reroute DASH flows by considering the current network capacity, available bandwidth of the paths, and bitrate of the segments in order to provide high quality of experience (QoE) and fairness among DASH clients. Simulations performed under various network conditions show that the proposed study provides higher QoE and fairness compared with the max‐flow routing approach.

Cross-layer Optimization for Video Transmission using MDC in Wireless Mesh Networks

Abstract In this paper, we propose a cross-layer optimization technique for improving the QoS of video streaming applications in multi-radio Wireless Mesh Networks (WMNs) using Multiple Description Coding (MDC). WMN is an emerging technology which is used to connect various types of networks to the internet. As the trafficdensity is very high fromthe client mesh to the gateways, load balancing in these networks has become an important research issue. Thus, there is a need for multipath routing which can exploit the multi-radio capabilities along with the load balancing mechanism. Furthermore, the dynamic and shared nature of wireless medium makes the task of supporting QoS for video streaming applications challenging. Towards this,MDCis an effective technique for the video transmission over the unpredictable routes because of varying channel conditions.In this work,we compute multiple paths using Passive Interference andDelay Aware (PIDA) metric and performload balancing using MDC. Proposed work is implemented in Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol using NS2 simulator. The results reveal that proposed technique withMDCperforms better in terms of PSNR, frame delay and frame loss compared to existing technique

VTIM: Video Title Identification Using Open Metadata

Video-streaming applications are very popular these days. Existing studies of video streaming have attempted to identify video titles of users using machine learning techniques to identify specific patterns of video packets transmitted over the network. However, these studies have limitations when applied to actual environments where the network is congested or there are multiple users in the same network. This paper proposes Video Title Identification using open Metadata (VTIM), which identifies video titles by analyzing storyboards and Media Presentation Description (MPD) of MPEG-DASH in connection with video packets transmitted over the network. Attack was carried out using VTIM on 13,291 videos selected from actual video-streaming environment of YouTube. Our experiments show that VTIM is able to identify video titles with 100% accuracy at nearly thirty times faster than existing methods based on machine learning techniques. The paper also proposes and evaluates a countermeasure against VTIM.

Inferring Streaming Video Quality from Encrypted Traffic

Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so. We develop models that infer quality metrics (\ie, startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (\eg, due to aggregation). Second, we develop a single composite model that works for a range of different services (\ie, Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (\eg, the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed'' and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.

Video streaming application design for color blind users

Nowadays Video Streaming application becomes more popular in the word. Unfortunately, there is still few Video Streaming application that Color Blind Users friendly. The Color Blind Users can not enjoy Video Streaming application due to limitation color they can see. The purpose of this study is to investigate the design of Video Streaming application for Color Blind Users, so that it is expected that the Video Streaming application design can provide the same experience for users when using online video streaming web. The descriptive analysis was used as the method to analyze the research data. Interview with the Color Blind Users was conducted to sharpen the data analysis. The results of the research indicated that the design of Video Streaming application should be made in specific color. There are some color can be seen clearer by the Color Blind Users compare to the other colors. The result of the research contribute literature for building Video Streaming application in the right design to make the blind more convenient to play game approaching the normal situation.

Toward Optimal Grouping and Resource Allocation for Multicast Streaming in LTE

Multimedia traffic is predicted to account for $\mathbf {82}$ % of the total data traffic by the year 2020. With the increasing popularity of video streaming applications like YouTube, Netflix, Amazon Prime Video, popular video content is often required to be delivered to a large audience simultaneously. Multicast transmission can be used to cater to such applications efficiently. The common content can be transmitted to the users on the same resources resulting in considerable resource conservation. This paper proposes various schemes for efficient grouping and resource allocation for multicast transmission in LTE. The optimal grouping and resource allocation problems are shown to be NP-hard and so, we propose heuristic algorithms for both these problems. We also formulate a Simulated Annealing based algorithm to approximate the optimal resource allocation for our problem. The LP-relaxation based resource allocation proposed by us results in allocations very close to the estimated optimal.

Modelling time-dependent aggregate traffic in 5G networks

Future wireless networks like 5G will carry an increasingly wide variety of data traffic, with different QoS requirements. In addition to conventional data traffic generated from HTTP, FTP and video streaming applications by mobile broadband users [human-type communication (HTC)], traffic from machine-to-machine (M2M) and Internet-of-Things (IoT) applications [machine-type communication (MTC)] has to be supported by 5G networks. Time-of-day variation in arrival rate of connection-level requests and randomness in length of data sessions in HTC result in randomness in aggregate traffic. In MTC, randomness in traffic arises from random number of devices trying to connect to the base station at any given time. Traffic generated by MTC devices may be either periodic or event-triggered. Nevertheless, it is difficult to model aggregate traffic due to non-stationary nature of traffic generated by each type of service. In this paper, special correlation functions of stochastic point processes called Product Densities (PDs) are used for estimating aggregate traffic under non-stationary arrival rates. For HTC, PDs are defined for estimating time-dependent offered load of connection-level service requests and expected number of ON periods in an interval of time $$(0,T)$$. The aggregate traffic is evaluated for light-tail (exponential) and heavy-tail (hyper exponential) servicing times. For MTC, PDs are defined for estimating the random number of devices connected to the base station at any time. Another QoS parameter of interest in high speed networks is the expected number of service requests/devices delayed beyond a critical value of delay. Bi-variate PD is defined to estimate the number of service requests/devices delayed beyond a given critical threshold. The results from PD model show close agreement with simulation results. The proposed PD technique proves effective in performance analysis under time-dependent traffic conditions, and is versatile for application to several studies in wireless networks including power consumption, interference and handover performance.

Using Session Modeling to Estimate HTTP-Based Video QoE Metrics From Encrypted Network Traffic

Understanding the user-perceived quality of experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based adaptive streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model an HAS session and estimate video QoE metrics, such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth collected using a lab testbed for video sessions from three popular services, two video on demand (VoD) and one Live. eMIMIC estimates re-buffering ratio within 1% point of ground truth for up to 75% sessions in VoD (80% in Live) and average bitrate with error under 100 Kb/s for up to 80% sessions in VoD (70% in Live). We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%–3.2% higher accuracy and re-buffering ratio with 9.8%–24.8% higher accuracy without requiring any training on ground truth QoE metrics. Finally, we show that eMIMIC can estimate real-time QoE metrics with at least 89.6% accuracy in identifying buffer occupancy state and at least 85.7% accuracy in identifying average bitrate class of recently downloaded chunks.

Janus: A Multi-TCP Framework for Application-Aware Optimization in Mobile Networks

As the dominant protocol on the Internet, TCP has attracted significant attention and has been implemented in various ways, each of which optimizes for a single objective such as high throughput or low delay. However, in today's mobile networks that carry traffic from diverse types of flows, this approach may lead to misconfiguration of TCP congestion control algorithms and further degrade performance for many applications. In this paper, we propose Janus, a new transport-layer framework that automatically selects among existing congestion control variants to optimize traffic in accordance with application demands. Janus is easy to deploy because it reuses existing, well-tested congestion control implementations, and does not require any in-network or client-side changes. To explore the potential for this approach, we implement Janus in the Linux kernel and extensively evaluate its performance with both emulated and real Internet traffic. We show Janus outperforms alternative protocols by offering fast convergence times in response to changing network conditions, achieving 5-10X lower delay with comparable or higher throughput. Our approach also significantly improves user-perceived performance according to QoE metrics, with up to 5X fewer interruptions for video streaming applications and 2X faster page loading for web-browsing applications.

Generalization of LRU Cache Replacement Policy with Applications to Video Streaming

Caching plays a crucial role in networking systems to reduce the load on the network and is commonly employed by content delivery networks (CDNs) to improve performance. One of the commonly used mechanisms, Least Recently Used (LRU), works well for identical file sizes. However, for asymmetric file sizes, the performance deteriorates. This article proposes an adaptation to the LRU strategy, called gLRU, where the file is sub-divided into equal-sized chunks. In this strategy, a chunk of the newly requested file is added in the cache, and a chunk of the least-recently-used file is removed from the cache. Even though approximate analysis for the hit rate has been studied for LRU, the analysis does not extend to gLRU, since the metric of interest is no longer the hit rate as the cache has partial files. This article provides a novel approximation analysis for this policy where the cache may have partial file contents. The approximation approach is validated by simulations. Further, gLRU outperforms the LRU strategy for a Zipf file popularity distribution and censored Pareto file size distribution for the file download times. Video streaming applications can further use the partial cache contents to help the stall duration significantly, and the numerical results indicate significant improvements (32%) in stall duration using the gLRU strategy as compared to the LRU strategy. Furthermore, the gLRU replacement policy compares favorably to two other cache replacement policies when simulated on MSR Cambridge Traces obtained from the SNIA IOTTA repository.

Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning

In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used in dynamic adaptive streaming video applications. In this setting, we analyze power control policies considering the following two objectives: 1) the minimization of the transmit power consumption, and 2) the minimization of the transmission completion time of the communication session. In order to play the video without interruptions, the power control policy should also satisfy the requirement that the VBR video data is delivered to the mobile user without causing playout buffer underflow or overflows. A directional water-filling algorithm, which provides a simple and concise interpretation of the necessary optimality conditions, is identified as the optimal offline policy. Following this, two online policies are proposed for power control based on channel side information (CSI) prediction within a short time window. Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session. Subsequently, reinforcement learning (RL) based approach is employed for the second online power control policy. Via simulation results, we show that the optimal offline power control policy that minimizes the overall power consumption leads to substantial energy savings compared to the strategy of minimizing the time duration of video streaming. We also demonstrate that the RL algorithm performs better than the dynamic programming based online grouped water-filling (GWF) strategy unless the channel is highly correlated.

Attention Economy in Video Streaming Application: Avatar Interaction in YouTube Video Content Monetization

This article would like to unveil avatar interactions under the attention economy framework in online video streaming applications, taking YouTube as example, and how it benefits content creators to monetize their channels’ contents. T he theory of attention economy is proposed to explain how these avatar interactions in video streaming applications are perceived as a mode of transaction by using attention as universal currency. This article is elaborated using the social information processing theory to identify content creators’ avatar credibility through the application of cues during computer-mediated communication. To enrich the concept of credibility and methodology, the prominence-interpretation theory is also proposed . The method applied to this study is qualitative content analysis by examining five monetized Indonesian YouTube channels. In coding stage, several elements of the channels were identified , such as channel age, number of followers, and number of videos. Then from each channel, the video with the biggest number of views was picked and the types of advertisement showing up in such videos were examined . The observation found that five biggest YouTube channels in Indonesia in terms of number of subscribers had monetized their videos. Their videos had either overlay or non-stoppable video ad content during playback . This showed that number of views and engagements were generated from interactions between the content creators’ and visitors’ avatars. Attention metrics served as metrics in the attention economy framework, which in turn contribute to monetization — m aking it possible to see that YouTube channel s were standardized to meet advertisers’ requirements.

Attention Economy in Video Streaming Application: Avatar Interaction in YouTube Video Content Monetization

This article would like to unveil avatar interactions under the attention economy framework in online video streaming applications, taking YouTube as example, and how it benefits content creators to monetize their channels’ contents. T he theory of attention economy is proposed to explain how these avatar interactions in video streaming applications are perceived as a mode of transaction by using attention as universal currency. This article is elaborated using the social information processing theory to identify content creators’ avatar credibility through the application of cues during computer-mediated communication. To enrich the concept of credibility and methodology, the prominence-interpretation theory is also proposed . The method applied to this study is qualitative content analysis by examining five monetized Indonesian YouTube channels. In coding stage, several elements of the channels were identified , such as channel age, number of followers, and number of videos. Then from each channel, the video with the biggest number of views was picked and the types of advertisement showing up in such videos were examined . The observation found that five biggest YouTube channels in Indonesia in terms of number of subscribers had monetized their videos. Their videos had either overlay or non-stoppable video ad content during playback . This showed that number of views and engagements were generated from interactions between the content creators’ and visitors’ avatars. Attention metrics served as metrics in the attention economy framework, which in turn contribute to monetization — m aking it possible to see that YouTube channel s were standardized to meet advertisers’ requirements.

A Quality-Retaining Power-Saving Framework for Video Applications on Mobile OLED Displays

As people increase their dependency on mobile applications and services, saving power consumption of mobile devices becomes an important challenge for supporting video streaming applications. This paper investigates how to minimize the power consumption of an organic light-emitting diode (OLED) display when displaying a video under consideration for the user’s visual experience. We first model the minimization problem as an OLED video scaling optimization problem. We then propose an algorithm to solve the optimization problem and prove that the algorithm is optimal in terms of power savings. We next introduce an energy-saving cloud service based on the algorithm. Finally, the results of the extensive experiments conducted on a commercial mobile smartphone with four real-world videos to evaluate the performance of the proposed algorithm are very encouraging.

Distributed Caching Algorithms in the Realm of Layered Video Streaming

Distributed caching architectures have been proposed for bringing content close to requesters, and the key problem is to design caching algorithms for reducing content delivery delay, which determines to an extent the user Quality of Experience (QoE). This problem obtains an interesting new twist with the advent of advanced layered-video encoding techniques such as Scalable Video Coding. In this paper, we show that the problem of finding the caching configuration of video encoding layers that minimizes delivery delay for a network operator is NP-Hard, and we establish a pseudopolynomial-time optimal solution by using a connection with the multiple-choice knapsack problem. Next, we design caching algorithms for multiple network operators that cooperate by pooling together their co-located caches, in an effort to aid each other, so as to avoid large delays due to fetching content from distant servers. We derive an approximate solution to this cooperative caching problem by using a technique that partitions the cache capacity into amounts dedicated to own and other operators’ caching needs. Trace-driven evaluations demonstrate up to 25 percent reduction in delay over existing caching schemes. As a side benefit, our algorithms achieve smoother playback for video streaming applications, with fewer playback stalls and higher decoded quality.