The Common Media Application Format (CMAF) is a standard for adaptive bitrate live streaming. The CMAF adapts chunk encoding and enables low-latency live streaming. However, conventional bandwidth estimation for adaptive bitrate streaming underestimates bandwidth because download time is affected not only by network bandwidth but also by the idle times between chunks in the same segment. Inaccurate bandwidth estimation decreases the quality of experience of the streaming client. In this paper, we propose a chunk-grouping method to estimate the available bandwidth for adaptive bitrate live streaming. In the proposed method, by delaying HTTP request transmission and bandwidth estimation using grouped chunks, the client estimates the available bandwidth accurately due to there being no idle times in the grouped chunks. In addition, we extend the proposed method to dynamically change the number of grouping chunks according to buffer length during downloading of the previous segment. We evaluate the proposed methods under various network conditions in order to confirm the effectiveness of the proposed methods.

Recently, information centric network (ICN) has attracted attention because cached content delivery from router's cache storage improves quality of service (QoS) by reducing redundant traffic. Then, adaptive video streaming is applied to ICN to improve client's quality of experience (QoE). However, in the previous approaches for the cache control, the router implicitly caches the content requested by a user for the other users who may request the same content subsequently. As a result, these approaches are not able to use the cache effectively to improve client's QoE because the cached contents are not always requested by the other users. In addition, since the previous cache control does not consider network congestion state, the adaptive bitrate (ABR) algorithm works incorrectly and causes congestion, and then QoE degrades due to unnecessary congestion. In this paper, we propose an explicit cache placement notification for congestion-aware adaptive video streaming over ICN (CASwECPN) to mitigate congestion. CASwECPN encourages explicit feedback according to the congestion detection in the router on the communication path. While congestion is detected, the router caches the requested content to its cache storage and explicitly notifies the client that the requested content is cached (explicit cache placement and notification) to mitigate congestion quickly. Then the client retrieve the explicitly cached content in the router detecting congestion according to the general procedures of ICN. The simulation experiments show that CASwECPN improves both QoS and client's QoE in adaptive video streaming that adjusts the bitrate adaptively every video segment download. As a result, CASwECPN effectively uses router's cache storage as compared to the conventional cache control policies.

Recently, adaptive streaming over information centric network (ICN) has attracted attention. In adaptive streaming over ICN, the bitrate adaptation of the client often overestimates a bitrate for available bandwidth due to congestion because the client implicitly estimates congestion status from the content download procedures of ICN. As a result, streaming overestimated bitrate results in QoE degradation of clients such as cause of a stall time and frequent variation of the bitrate. In this paper, we propose a congestion-aware adaptive streaming over ICN combined with the explicit congestion notification (CAAS with ECN) to avoid QoE degradation. CAAS with ECN encourages explicit feedback of congestion detected in the router on the communication path, and introduces the upper band of the selectable bitrate (bitrate-cap) based on explicit feedback from the router to the bitrate adaptation of the clients. We evaluate the effectiveness of CAAS with ECN for client's QoE degradation due to congestion and behavior on the QoS metrics based on throughput. The simulation experiments show that the bitrate adjustment for all the clients improves QoE degradation and QoE fairness due to effective congestion avoidance.

In this paper the quality of adaptive bit rate video streaming is investigated and two state-of-the-art models, i.e., the NTT audiovisual quality-estimation and ITU-T P.1203 models, are considered. This paper shows how these models can be applied to new conditions, e.g., 4K ultra high definition (4K-UHD) videos encoded using H.265, considering that they were originally designed and trained for HD videos encoded with H.264. Six subjective evaluations involving up to 192 participants and a large variety of test conditions, e.g., durations from 10sec to 3min, coding-quality variation, and stalling events, were conducted on both TV and mobile devices. Using the subjective data, this paper addresses how models and coefficients can be transferred to new conditions. A comparison between state-of-the-art models is conducted, showing the performance of transferred and retrained models. It is found that other video-quality estimation models, such as VMAF, can be used as input of the NTT and ITU-T P.1203 long-term pooling modules, allowing these other video-quality-estimation models to support the specificities of adaptive bit-rate-streaming scenarios. Finally, all retrained coefficients are detailed in this paper allowing future work to directly reuse the results of this study.

This paper focuses on the bandwidth allocation methods based on real user experience for web browsing applications. Because the Internet and its services are rapidly increasing, the bandwidth allocation problem has become one of the typical challenges for Internet service providers (ISPs) and network planning with respect to providing high service quality. The quality of experience (QoE) plays an important role in the success of services, and the guarantee of QoE accordingly represents an important goal in network resource control schemes. To cope with this issue, this paper proposes two user-centric bandwidth resource allocation methods for web browsing applications. The first method dynamically allocates bandwidth by considering the same user's satisfaction in terms of QoE with respect to all users in the system, whereas the second method introduces an efficient trade-off between the QoE of each user group and the average QoE of all users. The purpose of these proposals is to provide a flexible solution to reasonably allocate limited network resources to users. By considering service quality from real users' perception viewpoint, the proposed allocation methods enable us to understand actual users' experiences. Compared to previous works, the numerical results show that the proposed bandwidth allocation methods achieve the following contributions: improving the QoE level for dissatisfied users and providing a fair distribution, as well as retaining a reasonable average QoE.

In this paper we propose an adaptive bitrate (ABR) algorithm that selects the video rates by observing and controlling the playback buffer. In a Hypertext Transfer Protocol (HTTP) adaptive streaming session, buffer dynamics largely depend on the chunk sizes. First, we present the algorithm that selects the next video rates based on the current buffer level, while considering the upcoming chunk sizes. We aim to exploit the variation of chunk sizes of a variable bitrate (VBR) encoded video to optimize the tradeoff between the video rate and buffer underflow events while keeping a low frequency of video rate changes. To evaluate the performance of the proposed algorithm, we consider three scenarios: (i) the video flow does not compete with any cross traffic, (ii) the video flow shares the bottleneck link with competing TCP traffic, and (iii) two HTTP clients share the bottleneck. We show that the proposed algorithm selects a high playback video rate and avoids unnecessary rebuffering while keeping a low frequency of video rate changes. Furthermore, we show that the proposed algorithm changes the video quality gradually to guarantee the user's viewing experience.

The IEEE 802.11 wireless LAN (WLAN) is based on carrier sense multiple access with collision avoidance (CSMA/CA) protocol. CSMA/CA uses a backoff mechanism to avoid collisions among stations (STAs). One disadvantage of backoff mechanisms is that STAs must wait for some period of time before transmission, which degrades spectral efficiency. Moreover, a backoff algorithm cannot completely avoid collisions. We have proposed a novel medium access control (MAC) scheme called the visual recognition-based medium access control (VRMAC) scheme, which uses an LED-camera communication technique. STAs send media-access request messages by blinking their LEDs in VRMAC scheme. An access point (AP) receives the messages via its camera, and then allocates transmission opportunities to the STAs by transmitting control frames. Since the transmission rate of the LED-camera communication is lower than WLAN transmission, the delay of access requesting causes and it could decrease the system throughput of the VRMAC system based WLAN. We reveal the effect of the delay for TCP flows and propose enhanced access procedures to eliminate the effect of the delay. Our simulation results demonstrate that VRMAC scheme increases the system throughput in UDP and TCP traffic. Moreover, the scenario-based evaluations reveal that VRMAC scheme also decreases the session delay which is a metric of quality of experience (QoE) for TCP applications.

Mobile social networks (MSN) provides diverse services to meet the needs of mobile users, i.e., discovering new friends, and sharing their pictures, videos and other information among their common interest friends. On the other hand, Quality-of-Experience (QoE) is a new concept related to but differs from Quality-of-Service (QoS) perception. QoE is a subjective measure of a customer's experiences with a service focuses on the entire service experience, and is a more holistic evaluation. So far, QoS issues have been focused and mainly addressed in the literature of MSNs. To the best of our knowledge, this paper is the first article to address QoE issues in emerging MSNs. In this paper, we first present a comprehensive investigation on recent advances in MSNs as well as QoE issues addressed in various types of applications and networks. From the lessons learned from the literature, then we propose a future research direction of QoE in MSNs.

With the popularity of BitTorrent-like P2P applications, improving its performance has been an active research area. Super-seeding, a special upload policy for the initial seeder, improves the efficiency in producing multiple seeds and reduces the uploading bytes of content initiators, thus being highly expected as a promising solution for improving downloading performance while decreasing uploading cost. However, the overall impacts of super seeding upon BitTorrent performance remain a question and have not been analyzed so far in literature. In this paper, we present an analytical and experimental study over the performance of super-seeding scheme. We attempt to answer the following questions: whether and how much super-seeding saves uploading cost, whether the overall downloading time is decreased by super-seeding, and in which circumstances super-seeding performs worse. Based on the seeding process, our analytical study gives formulas on the new piece distribution time, average downloading time and minimum distribution time for heterogeneous P2P file distribution system with super-seeding. Robust evidence supporting the use (or not) of super-seeding is given based on our worldwide Internet experiments over wide distribution of 250 PlanetLab nodes. With a well-designed experimental scenario, we study the overall download time and upload cost of super seeding scheme under varying seed bandwidth and peer behavior. Results show that super-seeding can save an upload ratio of 20% and does help speeding up swarms in certain modes. Tentative conclusions about the effectiveness of super-seeding and its optimal working circumstances are given with inside mechanism analyzed and negative factor identified. Our work not only provides reference for the potential adoption of super-seeding in BitTorrent and other P2P applications, but also much insights for the tussle of enhancing of Quality of Experience (QoE) and saving cost for a large-scale BitTorrent-like P2P commercial application.