June 7, 2012,
Thursday
Debates Room,
the Hart House
|
Keynote Address
1
Dr. Ali C. Begen, Cisco Systems
9:00 a.m.
— 10:00 a.m.
TV Everywhere [Presentation]
Chair: Baochun Li,
University of Toronto
Abstract: As more and more PC and handheld like devices get connected, consumers are migrating to the
Web to watch their favorite shows and movies. Increasingly, the Web is coming to digital TV,
which incorporates movie downloads and streaming. Similarly, consumers also want their TV
content on alternative devices. What does this mean for service and content providers? What do
they have to do to not lose their subscribers and revenue streams? This talk overviews the TV
Everywhere technologies available for integrating the emerging over-the-top content into a
managed network and making premium content accessible for unmanaged devices. The talk also
provides a few real-world use cases.
|
Session 1:
Streaming I
10:30 a.m.
— 12:30 p.m.
Session Chair: Gwendal Simon (Telecom Bretagne)
|
Why Are
State-of-the-art Flash-based Multi-tiered Storage Systems Performing Poorly
for HTTP Video Streaming? [Presentation]
Moonkyung Ryu, Hyojun Kim,
Umakishore Ramachandran
(Georgia Institute of Technology)
Abstract: MLC
ash memory is a promising technology for building
a high-performance and cost-effective video streaming system when it is used as an intermediate level cache in a
multi-tiered storage hierarchy. Therefore, we were quite surprised when through extensive measurements we found that
two state-of-the-art
ash-based multi-tiered storage systems
(namely,
flashcache and ZFS) have quite disappointing performance for HTTP video streaming using the DASH protocol. We have conducted a thorough analysis to understand
the reasons for the poor performance of these two systems.
In a nutshell, unless attention is paid to the unique performance characteristics of
ash memory-based SSDs, we
could end up with suboptimal or even poor performance as
we discovered through experimentation with these two systems. Based on the analysis, we present design guidelines
for building a cost-effective high-performance HTTP video
streaming server.
What Happens
when HTTP Adaptive Streaming Players Compete for Bandwidth? [Presentation]
Saamer Akhshabi,
Lakshmi Anantakrishnan, Constantine Dovrolis (Georgia Institute of Technology), Ali C. Begen (Cisco Systems)
Abstract: With an increasing demand for high-quality video content
over the Internet, it is becoming more likely that two or
more adaptive streaming players share the same network
bottleneck and compete for available bandwidth. This competition can lead to three performance problems: player instability, unfairness between players, and bandwidth underutilization. However, the dynamics of such competition and
the root cause for the previous three problems are not yet
well understood. In this paper, we focus on the problem of
competing video players and describe how the typical behavior of an adaptive streaming player in its Steady-State,
which includes periods of activity followed by periods of inactivity (ON-OFF periods), is the main root cause behind the
problems listed above. We use two adaptive players to experimentally showcase these issues. Then, focusing on the
issue of player instability, we test how several factors (the
ON-OFF durations, the available bandwidth and its relation
to available bitrates, and the number of competing players)
affect stability.
Interactions
Between HTTP Adaptive Streaming and TCP [Presentation]
Jairo Esteban, Steven Benno, Andre Beck, Yang Guo,
Volker Hilt, Ivica Rimac
(Alcatel-Lucent Bell Labs)
Abstract: HTTP adaptive streaming (HAS) is quickly becoming a popular
mechanism for delivering on-demand video content over the Internet. The chunked transmission and application-layer adaptation
create a very different traffic pattern than traditional progressive
video downloads where the entire video is downloaded with a single request.
In this paper, we investigate experimentally the interplay between HAS and the network transport control protocol (TCP). We
investigate the impact of network delay on achievable throughput
and discover that HAS streams cannot fully utilize the available
bandwidth due to the start and stop nature of HAS traffic patterns
and its interaction with TCP. We investigate TCP pacing as a potential solution to this issue, particularly for packet losses that occur
as a result of bursting packets into the network at the start of a
transmission. We find that pacing can significantly increase a TCP
flow's congestion window but it does not necessarily translate into
higher throughput. Instead, we find that packet losses at the end of
chunk transmission have a greater impact on throughput.
SmartTransfer: Transferring Your Mobile
Multimedia Contents at the "Right" Time [Presentation]
Yichuan Wang, Xin
Liu (University of California, Davis), Angela Nicoara
(Deutsche Telekom R&D Laboratories USA), Ting-An Lin (National Tsing Hua University), Cheng-Hsin Hsu (National Tsing Hua University)
Abstract: Today's mobile Internet is heavily overloaded by the increasing demand and capability of mobile devices, in particular,
multimedia traffic. However, not all traffic is created equal,
and a large portion of multimedia contents on the mobile Internet is delay tolerant. We study the problem of capitalizing the content transfer opportunities under better network
conditions via postponing the transfers without violating the
user-specified deadlines. We propose a new framework called
SmartTransfer, which offers a unified content transfer interface to mobile applications. We also develop two scheduling
algorithms to opportunistically schedule the content transfers. Via extensive trace-driven simulations, we show that
our algorithms outperform a baseline scheduling algorithm
by far: up to 17 times improvement in upload throughput
and/or at most 20 dBm boost in signal strength. The simulation results also reveal various tradeoff between the two
proposed scheduling algorithms. We have implemented our
framework and one of the scheduling algorithms on Android,
to demonstrate their practicality and efficiency.
|
Session 2: Cloud
and Middleware Support
2:00 p.m.
— 3:30 p.m.
Session Chair:
Bernard Wong (University of Waterloo)
|
Quiver: A
Middleware for Distributed Gaming [Presentation]
Giuseppe Reina (Eurecom, France),
Ernst Biersack (Eurecom,
France), Christophe Diot (Technicolor, France)
Abstract: Massively multiplayer online games have become popular in the
recent years. Scaling with the number of users is challenging due
to the low latency requirements of these games. Peer-to-peer techniques naturally address the scalability issues at the expense of additional complexity to maintain consistency among players.
We design and implement Quiver, a middleware that allows an
existing game to be played in peer-to-peer mode with minimal changes
to the engine. Quiver focuses on achieving scalability by distributing the game state. It achieves consistency by keeping the state
synchronized among all the players. We have built a working prototype of Quake II using Quiver. We analyze the changes necessary
to Quake II and discuss how generic a software like Quiver can be.
Cloud
Transcoder: Bridging the Format and Resolution Gap between Internet Videos
and Mobile Devices [Presentation] [Demo]
Zhenhua Li (Peking University), Yan Huang,
Gang Liu, Fuchen Wang (Tencent
Research), Zhi-Li Zhang (University of Minnesota), Yafei Dai (Peking University)
Abstract: Despite the increasing popularity, Internet video streaming
to mobile devices is still challenging. In particular, there has
been a format and resolution "gap" between Internet videos
and mobile devices, so mobile users have high demand on
video transcoding to facilitate their specific devices. However, as a computation-intensive work, video transcoding is
greatly challenged by the limited battery capacity of mobile
devices. In this paper we propose and implement "Cloud
Transcoder", which utilizes an intermediate cloud platform to bridge the "gap" via its special and practical designs.
Specifically, Cloud Transcoder only requires the user to upload a video request rather than the video content. After
getting the video request, Cloud Transcoder downloads the
original video from the Internet, transcodes it on the user's
demand, and transfers the transcoded video back to the user
with a high data rate via the intra-cloud data transfer acceleration. Therefore, the mobile device only consumes energy
in the last step - fast retrieving the transcoded video from
the cloud. Running logs of our real-deployed system confirm
the efficacy of Cloud Transcoder.
A Content
Replication Scheme for Wireless Mesh Networks [Presentation]
Zakwan Al-Arnaout,
Qiang Fu, Marcus Frean
(Victoria University of Wellington, New Zealand)
Abstract: Wireless Mesh Networks (WMNs) extend Internet access in
areas where the wired infrastructure is not available. A problem that arises is the congestion around gateways, delayed access latency and low throughput. Therefore, object
replication and placement is essential for multi-hop wireless
networks. Many replication schemes are proposed for the Internet, but they are designed for CDNs that have both high
bandwidth and high server capacity, which makes them unsuitable for the wireless environment. Object replication has
received comparatively less attention from the research community when it comes to WMNs. In this paper, we propose
an object replication and placement scheme for WMNs. In
our scheme, each mesh router acts as a replica server in a
peer-to-peer fashion. Our scheme exploits graph partitioning to build a hierarchy from fine-grained to coarse-grained
partitions. The challenge is to replicate content as close as
possible to the requesting clients and thus reduce the access
latency per object, while minimizing the number of replicas. Using simulation tests, we demonstrate that our scheme
is scalable, performing well with respect to the number of
replica servers and the number of objects. The proposed
scheme can give improved performance in terms of convergence time, throughput, hop count and hit ratio.
|
Session 3: Multiview and Panoramic Video
4:00 p.m.
— 5:00 p.m.
Session Chair: Zimu Liu (University of Toronto)
|
Evaluation of
Distribution of Panoramic Video Sequences in the eXplorative
Television Project [Presentation]
Peter Quax, Panagiotis
Issaris, Wouter Vanmontfort, Wim Lamotte (Expertise Center for Digital Media / Hasselt
University)
Abstract: In this paper, a scalable solution is presented for distributing
panoramic video sequences to multiple viewers at high resolution and quality levels. In contrast to traditional broadcast
scenarios, panoramic video enables the content consumer to
manipulate the camera view direction and viewport size.
By segmenting the panoramic input video into a set of separate sequences, transporting them over standard delivery
channels and recombining them at end user side, bandwidth
utilization is optimized and the quality of the video that is
visualized is increased. The proposed solution, called the
segmentation approach, is thoroughly explained and evaluated versus a single-stream solution with regards to several
metrics, including bandwidth utilization, encoding speed,
objective quality levels and seeking performance.
Collaborative
View Synthesis for Interactive Multi-view Video Streaming [Presentation]
Fei Chen, Jiangchuan
Liu (Simon Fraser University), Edith Cheuk-Han Ngai (Uppsala University)
Abstract: Interactive multi-view video enables users to enjoy the video
from different viewpoints. Yet multi-view dramatically increases the video data volume and their computation, making realtime transmission and interactions a challenging task.
It therefore calls for efficient view synthesis strategies that
flexibly generate visual views. In this paper, we present a
collaborative view synthesis strategy for online interactive
multi-view video streaming based on Depth-Image Based
Rendering (DIBR) view synthesis technology, which generates a visual view with the texture and depth information on
both sides. Different from the traditional DIBR algorithm
for single view synthesis, we explore the collaboration relationship between different viewpoints synthesis for a range of
visual views generation, and propose Shift DIBR (S-DIBR).
In S-DIBR, only the projected pixels, rather than all the
pixels of the reference view, are utilized for next visual view
generation. Therefore, the computation complexity of projection transform, which is the most computation intensive
process in the traditional DIBR algorithm, is reduced to
fulfill the requirement of online interactive streaming. Experiment results validate the efficiency of our collaborative
view synthesis strategy, as well as the bandwidth scalability
of the streaming system.
|
June 8, 2012,
Friday
Debates Room,
the Hart House
|
Keynote Address
2
Professor
Wu-chi Feng, Portland State University
9:00 a.m.
— 10:00 a.m.
Streaming
Media Evolution: Where to now? [Presentation]
Chair: Baochun
Li, University of Toronto
Abstract: The notion of streaming media has been around for nearly two decades. From a research
novelty to now consuming over half of all Internet bandwidth, streaming media is now
much more commonplace. This keynote address will give my perspective on the history
and evolution of streaming media and lessons learnt. Since the introduction of digital
video in the early 1990s, our community has spent significant effort focused on the
development of scalable and adaptive mechanisms for streaming media. We have
witnessed techniques ranging from interactive multimedia streaming, stored video
streaming, peer-to-peer streaming, and remote tele-presence. Several common themes
have emerged in these technologies and as we move forward as a community, this
keynote will offer some thought and observations on applying lessons learnt to future
technologies and a handful of areas where streaming media can make significant impact
beyond where we are today.
|
Session 4:
Streaming II
10:30 a.m.
— 12:00 p.m.
Session Chair: Pal Halvorsen (University of Oslo, Norway)
|
On Tile
Assignment for Region-of-Interest Video Streaming in a Wireless LAN [Presentation]
Ravindra Guntur, Wei Tsang Ooi (National University of Singapore)
Abstract: We consider the following problem in this paper: A video is encoded as a set of tiles T and is streamed to multiple users via a onehop wireless LAN. Each user selects a region-of-interest (RoI), represented as a subset of T, in the video to watch. The RoI selected
by the users may overlap. Each tile may be multicast or unicast.
We define the tile assignment problem as: which subset of tiles
should be multicast such that every user receives, within a transmission deadline, the subset of tiles pertaining to the RoI the user
selected, while minimizing the number of unwanted tiles received
by users. We present and evaluate five tile assignment methods.
We show that: (i) minimizing transmission delay can lead to significant wasteful reception in the multicast group, (ii) using tile access probability to assign tiles frequently leads to assignments that
violate the deadline, and (iii) a fast, greedy, heuristic works well:
it performs close to the optimal method and can always find an assignment within the deadline (as long as such assignment exists).
Minimizing
Server Throughput for Low-Delay Live Streaming in Content Delivery Networks [Presentation]
Fen Zhou (Telecom Bretagne), Shakeel Ahamad (De Montfort University), Eliya
Buyukkaya (Telecom Bretagne), Raouf
Hamzaoui (De Montfort University), Gwendal Simon (Telecom Bretagne)
Abstract: Large-scale live streaming systems can experience bottlenecks within the infrastructure of the underlying Content
Delivery Network. In particular, the "equipment bottleneck"
occurs when the fan-out of a machine does not enable the
concurrent transmission of a stream to multiple other equipments. In this paper, we aim to deliver a live stream to a
set of destination nodes with minimum throughput at the
source and limited increase of the streaming delay. We leverage on rateless codes and cooperation among destination
nodes. With rateless codes, a node is able to decode a video
block of k information symbols after receiving slightly more
than k encoded symbols. To deliver the encoded symbols,
we use multiple trees where inner nodes forward all received
symbols. Our goal is to build a diffusion forest that minimizes the transmission rate at the source while guaranteeing
on-time delivery and reliability at the nodes. When the network is assumed to be lossless and the constraint on delivery
delay is relaxed, we give an algorithm that computes a diffusion forest resulting in the minimum source transmission
rate. We also propose an effective heuristic algorithm for
the general case where packet loss occurs and the delivery
delay is bounded. Simulation results for realistic settings
show that with our solution the source requires only slightly
more than the video bit rate to reliably feed all nodes.
To Chunk or
Not to Chunk: Implications for HTTP Streaming Video Server Performance [Presentation]
Jim Summers, Tim Brecht (University of Waterloo), Derek Eager
(University of Saskatchewan), Bernard Wong (University of Waterloo)
Abstract: Large amounts of Internet streaming video traffic are being delivered using HTTP to leverage the existing web infrastructure.
A fundamental issue in HTTP streaming concerns the granularity
of video objects used throughout the HTTP ecosystem (including
clients, proxy caches, CDN nodes, and servers). A video may be
divided into many files (called chunks), each containing only a few
seconds of video at one extreme, or stored in a single unchunked
file at the other.
In this paper, we describe the pros and cons of using chunked and
unchunked videos. We then describe a methodology for fairly comparing the performance implications of video object granularity at
web servers. We nd that with conventional servers (userver,
nginx and Apache) there is little performance difference between these two approaches. However, by aggressively prefetching and sequentializing disk accesses in the userver, we are
able to obtain up to double the throughput when serving requests
for unchunked videos when compared with chunked videos (even
while performing the same aggressive prefetching with chunked
videos). These results indicate that more research is required to ensure that the HTTP ecosystem can handle this important and rapidly
growing workload.
|
Session 5:
Content Sharing
1:30 p.m.
— 2:30 p.m.
Session Chair:
Wu-chi Feng (Portland State University)
|
Content and
Geographical Locality in User-Generated Content Sharing Systems [Presentation]
Kévin Huguenin
(EPFL), Anne-Marie Kermarrec (INRIA), Konstantinos Kloudas (INRIA),
Francois Taïani (Lancaster University)
Abstract: User Generated Content (UGC), such as YouTube videos,
accounts for a substantial fraction of the Internet traffic.
To optimize their performance, UGC services usually rely
on both proactive and reactive approaches that exploit spatial and temporal locality in access patterns. Alternative
types of locality are also relevant and hardly ever considered together. In this paper, we show on a large (more than
650,000 videos) YouTube dataset that content locality (induced by the related videos feature) and geographic locality,
are in fact correlated. More specifically, we show how the
geographic view distribution of a video can be inferred to
a large extent from that of its related videos. We leverage
these findings to propose a UGC storage system that proactively places videos close to the expected requests. Compared to a caching-based solution, our system decreases by
16% the number of requests served from a different country
than that of the requesting user, and even in this case, the
distance between the user and the server is 29% shorter on
average.
Video Sharing
in Online Social Network: Measurement and Analysis [Presentation]
Haitao Li, Haiyang
Wang, Jiangchuan Liu (Simon Fraser University)
Abstract: Online social networks (OSNs) have become popular destinations for connecting friends and sharing information. Recent statistics suggest that OSN users regularly share contents from video sites, and a significant amount of requests
of the video sites are indeed from them nowadays. These
behaviors have substantially changed the workload of online video services. To better understand this paradigm
shift, we conduct a long-term and extensive measurement
of video sharing in RenRen, the largest Facebook-like OSN
in China. In this paper, we focus on the video popularity distribution and evolution. In particular, we find that
the video popularity distribution exhibits perfect power-law
feature (while videos in YouTube exhibit a power-law waist
with a long truncated tail). Moreover, we observe that the
requests for the new published videos generally experience
two or three days latency to reach the peak value, and then
change dynamically with a series of unpredictable bursts
(while in YouTube, videos reach the global peak immediately after introduction to the system, and then the accesses
generally decrease overtime, except possibly on some special
days). These differences can raise new challenges to content providers. For example, the video popularity is now
hard to predict based on their historical requests. We further develop a simple yet effective model to simulate user
requests process across videos in OSNs. Trace-based simulation shows that it can well capture the observed features.
|
Panel
Discussion
2:30 p.m.
— 3:30 p.m.
![](photos/panel_small.jpg)
Pal Halvorsen (University of Oslo, Norway) [Presentation]
Wu-chi Feng (Portland State University) [Presentation]
Yan Huang (Tencent Inc.) [Presentation]
Tim Brecht (University of Waterloo) [Presentation]
Chair: Baochun Li (University of Toronto)
|
Session 6:
Video Compression
4:00 p.m.
— 5:30 p.m.
Session Chair: Quang Minh Khiem Ngo (National University of Singapore)
|
Sensor-Assisted
Camera Motion Analysis and Motion Estimation Improvement for H.264/AVC Video
Encoding [Presentation]
Guanfeng Wang, Haiyang
Ma, Beomjoo Seo, Roger
Zimmermann (National University of Singapore)
Abstract: Camera motion information is one aspect that helps to infer higher-level semantic descriptions in many video applications, e.g., in video retrieval. However, an efficient methodology for annotating camera motion information is still an
elusive goal. Here we propose and present a novel and efficient approach for the task of partitioning a video document
into sub-shots and characterizing their camera motion. By
leveraging location (GPS) and digital compass data, which
are available from most current smartphone handsets, we
exploit the geographical sensor information to detect transitions between two sub-shots based on the variations of both
the camera location and the shooting direction. The advantage of our method lies in its considerable accuracy. Additionally, the computational efficiency of our scheme enables
it to be deployed on mobile devices and to process videos
while recording. We utilize this capability to show how the
HEX motion estimation algorithm in the H.264/AVC encoder can be simplified with the aid of our camera motion
information. Our experimental results show that we can reduce the computation of the HEX algorithm by up to 50%
while achieving comparable video quality.
CAME:
Cloud-Assisted Motion Estimation for Mobile Video Compression and
Transmission [Presentation]
Yuan Zhao, Lei Zhang, Xiaoqiang Ma, Jiangchuan Liu (Simon Fraser University), Hongbo Jiang (Huazhong
University of Science and Technology)
Abstract: Video streaming has become one of the most popular networked applications and, with the increased bandwidth and
computation power of mobile devices, anywhere and anytime
streaming has become a reality. Unfortunately, it remains a
challenging task to compress high-quality video in real-time
in such devices given the excessive computation and energy
demands of compression. On the other hand, transmitting
the raw video is simply unaffordable from both energy and
bandwidth perspective.
In this paper, we propose CAME, a novel cloud-assisted
video compression method for mobile devices. CAME leverages the abundant cloud server resources for motion estimation, which is known to be the most computation-intensive
step in video compression, accounting for over 90% of the
computation time. With CAME, a mobile device selects and
uploads only the key information of each picture frame to
cloud servers for mesh-based motion estimation, eliminating
most of the local computation operations. We develop smart
algorithms to identify the key mesh nodes, resulting in minimum distortion and data volume for uploading. Our simulation results demonstrate that CAME saves almost 30%
energy for video compression and transmission.
Understanding
the Impact of Inter-Lens and Temporal Stereoscopic Video Compression [Presentation]
Wu-chi Feng, Feng
Liu (Portland State University)
Abstract: As we move toward more ubiquitous stereoscopic video,
particularly with multiple (> 2) lenses, the need to understand the
efficiency of compression will become increasingly important. In
this paper, we explore the impact of spatial (between lenses) and
temporal (over time) compression for stereoscopic video images.
In particular, because stereoscopic images are taken at the same
time, there is expected to be a high correlation between pixels in
the horizontal direction due to the fixed nature of the multiple
lenses. We propose a vertically reduced search window in order to
take advantage of this correlation. Starting with multiple
stereoscopic video sequences shot using a production studio 3D
camera, we explore the effectiveness of temporal and inter-lens
motion compensation for stereoscopic video. Furthermore, the
experiments use exhaustive search to remove the effects of
heuristic-based motion-compensation techniques.
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Closing Remarks
Baochun Li (University of Toronto), Co-chair
Welcome to NOSSDAV 2013
Pal Halvorsen (University of Oslo, Norway)
|