Hindawi Publishing Corporation
International Journal of Distributed Sensor Networks
Volume 2015, Article ID 518289, 11 pages
http://dx.doi.org/10.1155/2015/518289
Research Article
A Novel Energy-Efficiency Social-Inspired Video Sharing
Solution in Wireless Networks
Shijie Jia,1 Youzhong Ma,1 Yongxin Zhang,1 Lujie Zhong,2 and Changqiao Xu3
1
Academy of Information Technology, Luoyang Normal University, Luoyang, Henan 471022, China
Information Engineering College, Capital Normal University, Beijing 100048, China
3
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications,
Beijing 100876, China
2
Correspondence should be addressed to Lujie Zhong; [email protected]
Received 6 October 2015; Accepted 29 November 2015
Academic Editor: Athanasios V. Vasilakos
Copyright © 2015 Shijie Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Mobile multimedia streaming services provide rich-content visual resources for the mobile users via ubiquitous access to Internet.
The video resource sharing focuses on the matching of appropriate resource supplier for the resource requesters and is a key issue
for P2P-based video system scalability and user quality of experience. In this context, leveraging social-driven interaction between
mobile users enables the discovery of common interests to improve video content sharing efficiency. In this paper, we propose a
novel energy-efficiency social-inspired video sharing solution in wireless networks (ESVS). By the analysis of historical request
behaviors of users, ESVS designs an estimation method of relationship between videos and groups the videos into a chain-based
tree structure. Based on the constructed video tree, ESVS designs a hybrid resource lookup algorithm including push and pull
and a communication quality-aware selection strategy of video suppliers, which improves the communication capacities between
requesters and suppliers and reduces the network bandwidth consumption. Simulation results also show how ESVS achieves higher
resource lookup success rate, lower startup delay, less packet loss rate, and lower maintenance cost than another state-of-the-art
solution.
1. Introduction
Increasing wireless bandwidth and developmental network
technologies such as cellular networks, wireless mesh networks, wireless local networks, and mobile ad hoc networks
support the deployment of mobile multimedia streaming
services for the mobile users via ubiquitous access to Internet
[1]. The video streaming is one of most popular multimedia
services in Internet and contributes huge network traffic [2].
The increase in the user scale and the demand of watched
quality leads to severe consumption of network bandwidth
and influences scalability and quality of service (QoS) of
multimedia systems. Peer-to-Peer (P2P) technologies construct the logical link between mobile clients to achieve the
resource sharing, which relieves the load of media server
to improve the system scalability [3–7]. On the other hand,
the deployment of P2P-based video systems in wireless
networks also needs to address the problems of video delivery
performance caused by the mobility of mobile nodes. A key
issue is how to improve the scalability and communication
capacities of P2P networks in wireless networks in order
to promote the quality of experience (QoE) and energy
endurance of mobile users [8–12].
The existing P2P multimedia streaming solutions in
wireless mobile networks still use the traditional structures
to manage the video resources. For instance, in QUVoD [13],
the nodes which store available resources are grouped into a
chained Chord structure where these resources are deployed
in 4G networks. QUVoD relies on the DHT-based structure
and the similarity of stored resources between nodes to
achieve fast location of video content. However, QUVoD
does not address the problems of system scalability caused
by the increase in the number of mobile nodes. RACOM in
our previous work [14] built a mesh overlay network. Each
node maintains the state information of multiple nodes in
terms of playback state reliability and prediction results of
2
playback content to achieve fast resource search. Although
RACOM does not employ flooding search in the whole P2P
networks and avoids the high lookup delay, the redundancy
link between nodes increases the maintenance cost of nodes
and wastes network bandwidth.
The video content has very important influence for the
user lookup behaviors [15]. For instance, the popular videos
attract the large number of users to access them; the videos
in the TV series have high access probabilities. Making use of
the relationship between videos to group the resources in P2P
networks can promote the performance of resource sharing.
Finding relationship between videos and regulating the relationship in real time when the user interests change reduces
the “distance” between requesters and desired content, which
is very important for improving communication capacities of
P2P networks.
Social networks are emerging technologies, which build
the relationship between users in terms of the similarity in
the values and interest and family [16–19]. The interaction
in social networks mainly includes searching, forwarding,
and attention [20]. The searching denotes that the users
actively search the content from other contacted users. The
forwarding denotes that the users receive and forward the
content generated by other contacted users. The attention
denotes that the users are willing to receive the content from
other users. Obviously, the content sharing in social networks
is classified as pull and push (searching is an active pull,
and forwarding and attention are the passive push) [21]. The
hybrid searching pattern including pull and push speeds up
the content dissemination and improves the content sharing
efficiency.
In this paper, we propose a novel energy-efficiency socialinspired video sharing solution in wireless networks (ESVS).
By estimation of similarity between video content and the
analysis of request behaviors of users, ESVS calculates the
contact levels between videos and clusters the videos into a
chain-based tree structure in order to accurately push interested content to the users. ESVS designs a hybrid resource
lookup strategy, which makes use of video tree structure to
fast search the resource suppliers and accurately push content
in terms of user request, which reduces the lookup “distance”
between requester and suppliers. Further, by monitoring the
communication quality in video data transmission path, the
nodes in ESVS dynamically make the switchover between
suppliers in order to obtain high video delivery capacities
and improve utilization efficiency of network bandwidth.
Extensive tests show how ESVS achieves higher resource
lookup success rate, lower startup delay, less packet loss rate,
and lower maintenance cost than another state-of-the-art
solution.
2. Related Work
Recently, some solutions focus on the video sharing in
wireless mobile networks by making use of content similarity.
For instance, QUVoD in [13] makes use of the distributed
hash table (DHT) to cluster the nodes which store the
same or similar video chunks into a hybrid group-based
DHT structure. Therefore the request nodes can obtain
International Journal of Distributed Sensor Networks
the video resources in intragroups instead of the whole DHT
structure, which reduces the number of forwarding request
messages. However, because the video resources stored by the
intragroup nodes are sequential, the request nodes still need
to use the DHT structure when they search the chunks which
have long “distance” with current played chunks. Moreover,
all nodes are grouped into the DHT structure so that the
increase in the number of nodes brings high maintenance
cost and limits QUVoD’s scalability. In SURFNet [22], the
nodes with long online time form an AVL tree and store
the superchunk (relatively long length). The other nodes
store short video chunks relative to the superchunk and
form the chains which are attached to the corresponding
nodes in the AVL tree in terms of the similarity with the
superchunk. SURFNet makes use of the hybrid structure to
improve lookup performance of video content and balance
the node load. However, SURFNet also has the same problem
with QUVoD for the system scalability; namely, the increase
in the number of nodes in overlay networks brings huge
maintenance cost of overlay networks. RACOM [14] enables
the nodes to build the logical link in terms of the contact level
between video chunks. Because the nodes implement the
autonomous management for the logical link, the system has
high scalability. However, the increase in the number of nodes
leads to fast rise in the number of the logical links (including
the large number of redundancy links), which brings high
maintenance cost for the link and causes the overload of
nodes. QUVoD, SURFNet, and RACOM make use of the
similarity between content to construct the P2P networks
and obtain high gains for the resource lookup. However,
these solutions only estimate the content similarities between
chunks in the same video but do not address the similarities
between videos.
Some social network-based content (video) sharing solutions address the estimation of similarity between content
well. SPOON [23] extracts the keywords from the name
of files stored by the mobile nodes to construct interest
vectors of nodes. The nodes make use of the vector angle
cosine to calculate the similarity level of interests between
nodes. SPOON further makes use of the common interest
and interaction to estimate the contact level between mobile
nodes and constructs the node communities. SPOON assigns
the role for the community members in order to address
the resource request. However, SPOON does not mention
the estimation method of interaction in detail. Moreover, the
calculation of interest similarity between nodes only relies
on the keywords of file name. It is difficult to ensure high
accuracy of interest similarity estimation, which leads to
fragile community structure and brings negative influence
for system scalability and resource lookup performance.
SocialTube estimates interest similarity level according to the
number in the intersection of watched videos between source
nodes and users [24]. A source node and multiple users
which have high interest similarity level form a node group
where the users are classified into follower and nonfollower
according to the similarity level. The source nodes push some
video content to the users, which promotes the resource
sharing efficiency. However, SocialTube employs the similarity estimation method based on the number of watched
International Journal of Distributed Sensor Networks
videos. It is difficult to capture real common interests, which
leads to the low resource push success rate. The relationship
between source nodes and users is easily influenced by the
change of the video content of source nodes and dynamic user
interests; namely, the fragile member relationship also brings
high maintenance cost of member state and low efficiency
of content sharing. NetTube considers that the users which
request and download the same video have common interests
and form a node community (swarm) [25]. When the
members in community change current watched videos, they
move to another community. The construction cost is low
for the community structure, and the relationship estimation
and state maintenance of members have low complexity.
However, NetTube also has some disadvantages, such as high
maintenance cost of contacts between communities and low
efficiency of resource lookup.
Based on the above analysis, the estimation method of
relationship between nodes is very important for the stability
of community structure and the efficiency of resource sharing. Another key issue is how to handle the change of user
interests. This is because the change causes the reconstruction
of P2P network topologies and the assignment of suppliers,
which consumes the large number of network bandwidths
and resources of computing and storage of nodes. Therefore,
we investigate the content-based similarity between videos
and the playback behavior of users to estimate the contact
levels between videos. Making use of accurate relationship between videos can construct the stable P2P network
topologies, ensuring resource sharing efficiency (e.g., high
success rate of video lookup and push reduces the number of
forwarding request messages, which improve the utilization
efficiency of network bandwidth) and system scalability (e.g.,
low maintenance cost). Moreover, the estimation of communication quality in the data transmission path can also achieve
bandwidth-saving (e.g., the decrease in the retransmission of
data for TCP protocol) and low startup delay, which improves
the energy utilization of mobile nodes.
3. ESVS Detailed Design
The media server has strong capacities of computing, storage,
and high bandwidth relative to the nodes in P2P networks and
uses stored original video resources 𝑉𝑆 = (V1 , V2 , . . . , V𝑛 ) to
provide initial streaming data for the request nodes. When a
mobile node 𝑛𝑖 requests a video, it sends a request message
to the media server. The server records the information of
𝑛𝑖 into a node list 𝑁𝑆, including requested video file and
node ID, and assigns a supplier for 𝑛𝑖 from 𝑁𝑆. When 𝑛𝑖
receives the video data from the assigned supplier, it has
joined into the system. If 𝑛𝑖 makes the change in the playback
content, it preferentially searches the content suppliers from
the P2P networks. If 𝑛𝑖 quits the system, it sends a message
containing the information of played content and playback
time to the server. The server collects the information of
playback behaviors of all nodes to analyze the relationship
between video content in order to enhance the accuracy of
content push and resource lookup success rate.
3
3.1. Analysis of User Lookup Behavior. The interest drives
the users to request different video content, so there is the
relationship between content and user interest. Let V𝑖 =
(nam𝑖 , act𝑖 , intro𝑖 ) denote a video where nam𝑖 , act𝑖 , and intro𝑖
are the name, actor, and introduction of V𝑖 , respectively.
The feature-based definition method not only can accurately
describe the information of video, but also makes it easy
to match the similarity between videos. Because the feature
items are denoted by text, we extract the feature word and
use the vector angle cosine to calculate the similarity between
items. For instance, the similarity between name of two
videos V𝑖 and V𝑗 is defined as
󳨀󳨀→ 󳨀󳨀→
𝑓𝑤𝑖 ⋅ 𝑓𝑤𝑗
.
𝑆 (nam𝑖 , nam𝑗 ) = 󵄩
󵄩󵄩𝑓𝑤 󵄩󵄩󵄩 ⋅ 󵄩󵄩󵄩󵄩𝑓𝑤 󵄩󵄩󵄩󵄩
󵄩 𝑖󵄩 󵄩 𝑗󵄩
(1)
The similarity for other feature items of V𝑖 and V𝑗 uses
the above calculation method of nam𝑖 . The video name and
actor are key factors for the attraction of user access; namely,
the attention levels of video name and actor for the users
are more than those of introduction. Therefore, we assign
different weight values for three feature items in the following
calculation equation of video similarity:
𝑆 (V𝑖 , V𝑗 ) = 𝑆 (nam)𝛼 × 𝑆 (act)𝛽 × 𝑆 (intro)𝛾 ,
(2)
where 𝛼, 𝛽, and 𝛾 are the weight value of name, actor, and
introduction and meet the constraint condition: 𝛼, 𝛽, 𝛾 ∈
(0, 1), 𝛼 = 𝛽 < 𝛾, and 𝛼 + 𝛽 + 𝛾 = 1. The high
similarity between V𝑖 and V𝑗 denotes that the probability
of user access to V𝑖 is high when the users are interested
in V𝑗 . The content similarity of two videos supports the
construction of relationship between videos. ESVS makes
use of the relationship to build the one-to-many mapping
between videos; namely, a video V𝑖 builds multiple contacts
with other videos which have high similarity with V𝑖 .
Except the relationship between videos based on the
content similarity, the user request behaviors also describe the
potential contact between videos. After a user has watched
current video, she/he requests another video; namely, the
request behaviors are considered as an access path. For
instance, let 𝐿𝑆 = (log1 , log2 , . . . , log𝑚 ) denote the playback
log set where any item log𝑖 = (V𝑎 , V𝑏 , V𝑐 , . . . , V𝑘 ) is a playback
log of a node 𝑛𝑖 (log𝑖 denotes the accessed content of a user).
The accessed content is considered as an access path V𝑎 →
V𝑏 → V𝑐 ⋅ ⋅ ⋅ V𝑘 . V𝑏 have the direct contact with V𝑎 and V𝑐 ;
namely, the user probably accesses V𝑏 due to watched V𝑎 ; V𝑎
has indirect contact with V𝑐 , where V𝑎 and V𝑐 have two-hop
distance. The request behaviors (playback logs) are stored
by the nodes and are sent to the server when the nodes
quit the system. The server analyzes the collected playback
logs to build the relationship between videos and estimates
the probability of jump from a video to another video. The
distance between two videos V𝑖 and V𝑗 in log𝑘 (log𝑘 includes
V𝑖 and V𝑗 ) denotes the tight level of contact and is defined as
log𝑘
𝜏𝑖𝑗
=
1
𝛿
𝐷log𝑘 (V𝑖 , V𝑗 )
, 𝜏𝑖𝑗 ∈ (0, 1] , 𝛿 ∈ (0, +∞) ,
(3)
4
International Journal of Distributed Sensor Networks
where 𝐷log𝑘 (V𝑖 , V𝑗 ) returns the number of hops between V𝑖 and
V𝑗 in log𝑘 and 𝛿 is an impact factor to control the decrease
log
process of 𝜏𝑖𝑗 𝑘 . With the increase in the distance between
two videos, the tight levels of contacts decrease; otherwise,
the near distance denotes that two videos have high levels in
the contact compactness. The cumulative sum of tight level in
all logs which include V𝑖 and V𝑗 is defined as
𝑢
log
𝜏̂𝑖𝑗 = ∑𝜏𝑖𝑗 𝑐 ,
(4)
𝑐=1
where 𝑢 is the total number of logs which include V𝑖 and
V𝑗 . On the other hand, we consider the similarity between
two videos as the estimation parameter for the calculation of
video push probability. The push of similar videos in terms
of current playback content of users also improves the push
success rate. We employ a playback behavior-based video
similarity estimation method, namely, a transitive similarity.
For instance, V𝑎 and V𝑐 have two-hop distance in log𝑘 and the
transitive similarity between V𝑎 and V𝑐 is defined as 𝑆(V𝑎 , V𝑏 ) ×
𝑆(V𝑏 , V𝑐 ). The transitive similarity of any two videos in the
same log is defined as
log
𝜂𝑖𝑗 𝑘
=
𝐷log 𝑘 (V𝑖 ,V𝑗 )
sim (V𝑐 , V𝑐+1 ) .
∏
𝑐=𝑖
(5)
Further, the cumulative sum of tight level in all logs which
include V𝑖 and V𝑗 is defined as
𝑢
log
𝜂̂𝑖𝑗 = ∑𝜂𝑖𝑗 𝑐 .
(6)
𝑐=1
The cumulative sum of distance and transitive similarity
of V𝑖 and other contacted videos can be obtained according to
the above method. We use ant colony algorithm to calculate
the probability of access between V𝑖 and V𝑗 according to the
following equation:
𝜇
𝑃 (V𝑖 , V𝑗 ) =
𝜏̂𝑖𝑗𝜆 × 𝜂̂𝑖𝑗
𝜇
∑𝑠𝑐=1 𝜏̂𝑖𝑐𝜆 × 𝜂̂𝑖𝑐
,
(7)
𝑃 (V𝑖 , V𝑗 ) ∈ (0, 1] , 𝜆, 𝜇 ∈ (0, 1) ,
where 𝑠 is the number of videos which have the contact with
V𝑖 ; 𝜆 and 𝜇 are the impact factors of 𝜏 and 𝜂, respectively; and
𝑃(V𝑖 , V𝑗 ) denotes the probability of watching V𝑗 when the users
are playing V𝑖 . The video set whose items have the contact
with any video V𝑖 is defined as 𝑆V𝑖 = (V𝑎 , V𝑏 , . . . , Vℎ ) where V𝑖
is the head item of 𝑆V𝑖 and all items in 𝑆V𝑖 are sorted following
descending order in terms of the access probability. When a
user requests V𝑖 , the supplier recommends the items in 𝑆V𝑖 in
order to improve the sharing efficiency. Based on the above
method, the server can obtain the contacted video set of all
videos.
3.2. Video Clustering-Based Node Community. The video
clustering actually is to build a resource distribution mode
in order to obtain optimal resource lookup performance.
Before the video clustering, ESVS preprocesses the videos by
a self-learning process. A video may keep the contact with
multiple videos in other video sets. For instance, 𝑆V𝑖 and 𝑆V𝑘
include V𝑗 , and V𝑖 and V𝑘 have the contact with V𝑗 . When a
node 𝑛𝑎 searches V𝑖 and V𝑘 , V𝑗 is repeatedly recommended
to 𝑛𝑎 . Obviously, the low-efficiency push wastes network
bandwidth and computing resources of nodes. We hope that
the content push has high success rate in terms of user
interests; namely, the push content meets the user demand
when the users search other videos. We need to eliminate
the “noise” contacts between videos to enable the videos to
have single contact between videos in terms of the access
probability. For instance, V𝑖 and V𝑗 have higher 𝑃(V𝑖 , V𝑗 )
than 𝑃(V𝑘 , V𝑗 ) of V𝑘 and V𝑗 ; namely, the push success rate
with the high access probability is larger than that with low
probability. Therefore, the items with low access probability
in the contacted video set are considered as noise items which
should be removed in terms of the following rule.
Rule 1. If V𝑖 and V𝑘 have the same item V𝑗 in the contacted
video set and 𝑃(V𝑖 , V𝑗 ) > 𝑃(V𝑘 , V𝑗 ), V𝑘 deletes the contact with
V𝑗 and 𝑆V𝑘 removes V𝑗 .
By the elimination of noise contact according to Rule 1,
any video V𝑖 only keeps single contact with V𝑗 which has the
highest access probability with V𝑖 compared to that of other
videos. By the iteration of elimination of noise contact, there
are some sets which only have head items. Therefore, we
consider the mergence of video sets by the following rule.
Rule 2. If 𝑆V𝑗 only has single item V𝑗 and V𝑖 has the highest
access probability with V𝑗 compared to that of other videos,
𝑆V𝑗 is merged into 𝑆V𝑖 .
By the iteration of set mergence in terms of Rule 2, the
merged sets meet the requirement: the union set of all items
(including head items) in merged sets is equal to 𝑉𝑆; namely,
(V𝑎 ∪ 𝑆V𝑎 ) ∪ (V𝑏 ∪ 𝑆V𝑏 ) ⋅ ⋅ ⋅ (V𝑘 ∪ 𝑆V𝑘 ) = 𝑉𝑆. Any set forms a video
cluster in terms of video similarity and playback behaviors.
For instance, 𝑆V𝑖 forms a cluster 𝐶V𝑖 where V𝑖 still acts as
the head item of 𝐶V𝑖 . The head items in all clusters form a
binary tree and the other items in clusters build a chain and
connect with corresponding head items. The group strategy
for the nodes in the binary tree relies on the cumulative sum
of popularities of cluster members according to the following
equation:
|𝐶 |
𝑝𝐶𝑖 =
∑𝑒=1𝑖 𝑓V𝑒
∑𝑛𝑐=1 𝑓V𝑐
,
(8)
where 𝑓 is the access frequency of video; |𝐶𝑖 | returns the
number of videos in 𝐶𝑖 and 𝑛 is the number of all videos;
|𝐶 |
∑𝑒=1𝑖 𝑓V𝑒 denotes the cumulative sum of popularities of all
items in 𝐶𝑖 . All clusters are reordered in terms of their
popularities, namely, (𝐶V𝑎 , 𝐶V𝑏 , 𝐶V𝑐 , . . . , 𝐶V𝑘 ). The head item
in cluster 𝐶V𝑎 which has the largest cumulative popularities
compared to other clusters becomes the tree root node and
the other members in 𝐶V𝑎 form a chain and connect with V𝑎 .
International Journal of Distributed Sensor Networks
5
NC𝑡
NC𝑢
NC𝑎
NC𝑝
NC𝑦
NC𝑥
NC𝑞
NC𝑏
···
NC𝑐
···
···
Figure 1: Chain-based binary tree.
V𝑏 and V𝑐 are the left and right children of V𝑎 , respectively;
namely, the left child has higher cluster popularities than
those of right child. Based on the above method, we group
the surplus head items into the binary tree in terms of the
popularities. The nodes which play the same video form
a node community which includes one or multiple broker
members and ordinary members. For instance, a node subset
𝑁𝐶V𝑎 = (𝑛𝑖 , 𝑛𝑗 , . . . , 𝑛𝑘 ) forms the community corresponding
to V𝑎 . Because V𝑎 is the head item in 𝐶V𝑎 and has the contacts
with other items in 𝐶V𝑎 , 𝑁𝐶V𝑎 has multiple broker nodes to
maintain the contacts with the broker nodes in communities
corresponding to other items in 𝐶V𝑎 . Moreover, 𝑁𝐶V𝑎 also has
two broker nodes to maintain the contacts with the broker
nodes of 𝑁𝐶V𝑏 and 𝑁𝐶V𝑐 corresponding to V𝑏 and V𝑐 . The
communities corresponding to other items in 𝐶V𝑎 only have
one broker node to keep the contact with any broker node in
𝑁𝐶V𝑎 . As Figure 1 shows, 𝑁𝐶V𝑎 , which is the tree root, locates
at the top layer and keeps the contacts with the left and right
children 𝑁𝐶V𝑏 and 𝑁𝐶V𝑐 at the second layer; 𝑁𝐶V𝑢 and 𝑁𝐶V𝑡
form a chain and assign the broker members to maintain the
contacts with 𝑁𝐶V𝑎 .
The node communities at the high layers have high levels
in popularities and access probabilities of video content and
rely on short logical distance in tree to achieve fast resource
location. (1) Making use of the popularities to define the
relationship between node communities in tree enables the
videos with high-frequency access to keep the tight contacts.
For instance, the members in 𝑁𝐶V𝑢 search V𝑥 and the request
messages are quickly forwarded by the direct contact between
𝑁𝐶V𝑎 and 𝑁𝐶V𝑏 , which reduces the resource lookup delay.
(2) The integration for the node communities corresponding
to videos with high access probabilities at the same layer
not only reduces the lookup delay, but also improves the
lookup success rate. For instance, the request messages for V𝑢
or V𝑡 only need to be forwarded by the broker members in
𝑁𝐶V𝑎 . (3) The clustering and merging of videos reduces the
number of nodes in tree (limits the height of tree); namely,
the decrease in the number of rely nodes in the process
of forwarding request message reduces the resource lookup
delay.
3.3. Hybrid Searching. The node communities have one
or multiple broker members according to the number of
contacts between communities. If a community 𝑁𝐶V𝑢 only
has one broker member 𝑛𝑖 , 𝑛𝑖 needs to maintain the state
of ordinary members and the broker member 𝑛𝑗 in another
community 𝑁𝐶V𝑎 which keeps the contact with 𝑁𝐶V𝑢 . The
resource lookup for the mobile nodes and the members in
𝑁𝐶V𝑎 and other communities relies on 𝑛𝑖 .
(1) When the mobile nodes request V𝑢 and send request
messages to the server, the server records the information of request nodes into 𝑁𝑆 and returns the
response messages containing the information of
suppliers. Because the server does not maintain the
real-time state of nodes, the request nodes need to
inquire current state of multiple suppliers. The request
nodes connect with available suppliers and receive
video data and the suppliers forward the information
of request nodes to 𝑛𝑖 . 𝑛𝑖 records the information of
the request nodes which become ordinary members
of 𝑁𝐶V𝑢 , including the node ID and stored videos.
Moreover, 𝑛𝑖 pushes the videos to the request nodes
most likely to be interested in them by sending a
message containing the information of videos and
broker members corresponding to the communities
contacted with 𝑁𝐶V𝑢 .
(2) When the members in 𝑁𝐶V𝑎 request V𝑢 , 𝑛𝑗 receives the
request messages, adds its own information into the
messages, and forwards the messages to 𝑛𝑖 . The latter
assigns the suppliers and records the information of
request nodes and resource lookup path (the information of relay nodes in forwarded path of request
messages). Because 𝑁𝐶V𝑢 only keeps the contact with
𝑁𝐶V𝑎 , 𝑛𝑖 does not push the videos to the request
nodes.
(3) If the members in 𝑁𝐶V𝑥 request V𝑢 , the request
messages are firstly forwarded to the broker member
𝑛𝑘 of 𝑁𝐶V𝑏 , where 𝑛𝑘 is responsible for maintaining
the contact with 𝑁𝐶V𝑏 . 𝑛𝑘 continues to forward the
request messages to the broker member 𝑛ℎ in 𝑁𝐶V𝑎
where 𝑛ℎ is responsible for maintaining the contact
with 𝑁𝐶V𝑏 . When 𝑛ℎ finds that V𝑢 is a member in
the attaching chain, 𝑛ℎ pushes the information of V𝑎
and other videos in the chain to the request nodes
and forwards the request message to 𝑛𝑖 . 𝑛𝑖 receives the
request messages from 𝑛ℎ , records the information of
6
International Journal of Distributed Sensor Networks
request nodes and resource lookup path, and returns
a message including the information of suppliers.
If a community has multiple broker members, these
broker members have different task. For instance, 𝑛𝑗 is a
broker member of 𝑁𝐶V𝑎 and is responsible for maintaining
the state of the broker member 𝑛𝑖 of 𝑁𝐶V𝑢 . 𝑛𝑗 receives and
handles the request messages from the nodes in 𝑁𝐶V𝑢 or other
communities, assigns the suppliers and pushes the video
content for the request nodes, and maintains the state of
the request nodes. 𝑛𝑗 exchanges the information of broker
members of other connected communities in order to fast
forward request messages. The fast resource lookup can
reduce the startup delay and improve user QoE.
Once the request nodes become new members, they
record the information of broker members in current communities in order to search other resources. If the request
nodes are interested in pushed videos, they send the request
messages to corresponding broker members and prefetch
the video content into local prefetching buffer and also
become the members of communities corresponding to
the prefetched videos. Because any node may become the
member of multiple communities and record the information
of broker members of these communities, it bridges these
communities and acts as the associate broker member of
communities. The associate broker members share responsibility for the load of broker members (e.g., forwarding
request messages and maintaining members), which further
improves the scalability and resource sharing efficiency of
systems [26]. Moreover, the request nodes also calculate the
push success rate according to
𝑅𝑃𝑖𝑗 =
𝑓𝑖𝑗𝑠
𝑝,
(9)
𝑓𝑖𝑗
where 𝑓V𝑠𝑗 is the number of successful pushes of V𝑗 , 𝑓V𝑝𝑗
is the total number of pushing V𝑖 , and 𝑅𝑃V𝑖 denotes the
success rate of pushing V𝑗 when the nodes request V𝑖 . The
statistical information of push success rate is sent to the
server when the nodes quit the system. The server receives
the information of resource lookup path when the broker
members quit the system or become the ordinary members
in other communities. In order to timely make the regulation
for the tree structure in terms of the variation of user
interests, the server makes use of the push success rate and the
information of resource lookup path to recalculate the access
probability between two videos by transforming (7):
𝜇
𝑃 (V𝑖 , V𝑗 ) = 𝑅𝑃𝑖𝑗 ×
𝜏̂𝑖𝑗𝜆 × 𝜂̂𝑖𝑗
𝜇
∑𝑠𝑐=1 𝜏̂𝑖𝑐𝜆 × 𝜂̂𝑖𝑐
.
(10)
In order to reduce the calculation load, the server calculates the access probability among all videos according
to the period time 𝑇𝑢 and regulates the tree structure and
attached chains. The chain-based tree structure makes use of
the content similarity levels to group the videos. The change
of video relationship in tree relies on the recalculation of
video access probability, which ensures the high stability
of tree structure by setting 𝑇𝑢 . Although the dynamic user
demand causes the node movement between communities,
the changes of community structure do not bring any influence for the tree structure. Moreover, the videos in chain have
high similarity levels in content, so the movement of nodes
is kept in small range, which improves the content lookup
efficiency. On the other hand, the high-efficiency delivery
of video content also is very important for the user QoE.
In wireless heterogeneous networks, the complex network
environment and node mobility bring very severe negative
effects for the delivery of video content. For instance, the fast
increase in the data traffic of transmission path results in the
network congestion. The large number of video data losses
causes severe video distortion, which reduces the user QoE.
Due to the node mobility, the transmission distance change
from one hop to multiple hops leads to the increase in the
risk of decrease of transmission performance. ESVS employs
a periodical estimation method of communication quality
of transmission path to ensure high delivery performance
according to our previous work [27, 28]. When the packet loss
rate in the transmission path is higher than threshold 𝑇𝑙 , the
video data receivers disconnect from the suppliers and search
new suppliers. In order to avoid the jitter for the assignment
of suppliers, the broker members return the information of
multiple suppliers for the request nodes. The latter selects the
optimal suppliers in terms of the delivery capacities.
The change of played content causes the movement of
nodes from a community to another community, namely, the
mapping relationship between the members and a community changes to another community. For instance, when a
member 𝑛𝑑 in a community 𝑁𝐶V𝑢 changes current played
content from V𝑢 to V𝑎 , 𝑛𝑖 moves from 𝑁𝐶V𝑢 to 𝑁𝐶V𝑎 and
becomes new member of 𝑁𝐶V𝑎 . The broker member 𝑛𝑗 in
𝑁𝐶V𝑎 maintains the state of 𝑛𝑑 . If a community has multiple
broker members (multiple contacts with other communities),
the maintenance cost for the member state is distributed
to multiple broker members, which avoids the overload of
broker members and improves the scalability of communities. The communities with single broker member have
lower node traffic than those with multiple broker members.
On the other hand, if the communities with single broker
member have high popularities, the high node traffic also
leads to the increase in the load of broker members (even
if the associate broker members share responsibility for the
load of broker members). Therefore, we define a replacement
period time [cos(1/𝑝V𝑖 ) − 1] × (𝐿 V𝑖 − 𝑇𝑝 ) of broker members
for any community corresponding to V𝑖 . 𝑝V𝑖 ∈ (0, 1) is
the popularity of V𝑖 , 𝐿 V𝑖 is the length of V𝑖 , and 𝑇𝑝 denotes
the playback point when a community member becomes
the broker member. Due to cos(1/𝑝V𝑖 ) − 1 ∈ (0, 0.57),
the popularity-based replacement can avoid the overload
of broker members in energy. When the broker members
leave current communities or quit the system, they select
new broker members from the maintained members. The
new members which have long online time preferentially are
selected as the new broker members. The long online time
denotes that the members have stable playback state, which
avoids the jitter of replacement of broker members.
International Journal of Distributed Sensor Networks
7
90
Table 1: Simulation parameter setting for wireless network.
85
Values
4. Testing and Test Results Analysis
4.1. Simulation Settings and Scenarios. We compare the performance of ESVS with a state-of-the-art solution SURFNet
[22]. ESVS and SURFNet are deployed in the wireless network
whose settings are described in Table 1. The two solutions
are modeled and implemented in NS-2. The media server
stores 100 video files and the length of each file is set to 100 s.
The initial target location and speed of 300 mobile nodes
are randomly assigned. When the mobile nodes arrive at the
assigned target location, they continue to move according
to the reassigned target location and speed. We generate
the information of 100 videos (including name, actor, and
introduction) and 20,200 playback logs (including played
video ID and watched time) where the video information
and 20,000 playback logs are used to calculate the access
probabilities between videos. 200 mobile nodes join the
system following Poisson distribution and play video content
following generated 200 logs where the popularities of played
content meet the Zipf distribution and 50 nodes play 4
video files during the whole simulation time. When any node
finishes the playback, it quits the system. In ESVS, the value of
threshold 𝑇𝑙 is set to 0.35. Before starting the simulation, the
chain-based tree structure of ESVS has been built; namely, the
logical relationship between videos has been defined. When
the mobile nodes join the system, they form the communities
corresponding to the played videos. In SURFNet, the nodes
which have the long online time form AVL tree and the
nodes which play the same video form a chain and attach the
corresponding nodes in AVL tree.
4.2. Performance Evaluation. The performance of ESVS is
compared with that of SURFNet in terms of average resource
80
ARLSR (%)
1000 × 1000 m2
Area
Channel/WirelessChannel
Channel
Phy/WirelessPhyExt
Network interface
Mac/802 11
MAC interface
300
Number of mobile nodes
200
Number of mobile nodes playing video
[0, 30] m/s
Mobile speed range of nodes
500 s
Simulation time
200 m
Signal range of mobile nodes
Default distance between server and
6 hops
nodes
UDP
Transmission protocol
DSR
Wireless routing protocol
20 Mb/s
Bandwidth of server
10 Mb/s
Bandwidth of mobile nodes
128 kb/s
Transmission rate of video data
Random
Travel direction of mobile nodes
0s
Pause time of mobile nodes
75
70
65
60
55
50
50
100
150
200 250 300 350
Simulation time (s)
400
450
500
180
200
SURFNet
ESVS
Figure 2: ARLSR against simulation time.
85
80
75
ARLSR (%)
Parameters
70
65
60
55
50
45
40
20
40
60
80
100 120 140
Number of nodes
160
SURFNet
ESVS
Figure 3: ARLSR against number of nodes.
lookup success rate (ARLSR), startup delay, packet loss rate
(PLR), and maintenance cost, respectively.
(1) ARLSR. The event-request nodes send the lookup messages and successfully obtain the video content from the
P2P network which is defined as a success lookup. The ratio
between the number of successful lookups and the total
number of lookups denotes the resource lookup success rate.
The mean values of resource lookup success rate during a time
interval of 50 s and the process of node joining the system are
shown in Figures 2 and 3, respectively.
As Figure 2 shows, the two curves corresponding to the
results of ESVS and SURFNet have a fast increase trend
during the whole simulation time. SURFNet curve keeps fast
rise from 𝑡 = 50 s to 𝑡 = 250 s and maintains a relatively
slow increase from 𝑡 = 300 s to 𝑡 = 500 s. ESVS curve has
a relatively slow increase after a fast rise from 𝑡 = 50 s to
𝑡 = 300 s. The increment and peak value (83.8%) of ESVS are
larger than those of SURFNet.
Figure 3 also shows the increase trend of two curves with
increasing number of nodes. The blue curve of SURFNet
International Journal of Distributed Sensor Networks
4
3.5
3.5
3
3
Startup delay (s)
Startup delay (s)
8
2.5
2
1.5
1
2.5
2
1.5
1
0.5
50
100
150
200 250 300 350
Simulation time (s)
400
450
500
SURFNet
ESVS
0
20
40
60
80
100 120 140
Number of nodes
160
180
200
SURFNet
ESVS
Figure 4: Startup delay against simulation time.
Figure 5: Startup delay against number of nodes.
maintains a fast rise during the process of node joining the
system, but it also has an obvious fluctuation. ESVS curve
keeps higher levels than that of SURFNet and has larger
increment and peak value than those of SURFNet.
In SURFNet, the nodes with long online time are grouped
into an AVL tree and the other nodes which store the same
videos with the nodes in tree form the chain and attach
the tree. In initial simulation, the nodes which have joined
the system firstly form the AVL tree and obtain the video
resources from the media server. Therefore, the values of
SURFNet ARLSR keep the low levels. The increase in the
number of nodes provides the relatively enough available
resources for the new system members so that the values
of SURFNet ARLSR maintain fast rise. The change of user
interests for the video content brings the uncertainty of
resource demand; namely, some nodes still do not obtain the
requested resources from the P2P network and only receive
the video data from the server. Therefore, the ARLSR results
of SURFNet keep a stable slight increment after the fast
increase. ESVS groups the videos into a chain-based tree
structure and enables the nodes to form the communities
corresponding to the played videos. Moreover, the nodes
prefetch the videos of interest into local buffer and make
use of prefetched resource to serve other nodes; namely,
the prefetched content increases the available resources in
the P2P network. Therefore, the curve corresponding to the
results of ESVS ARLSR keeps the fast rise and has higher
increment than that of SURFNet.
of ESVS has a slight increase from 𝑡 = 50 s to 𝑡 = 100 s and
quickly decreases from 𝑡 = 100 s to 𝑡 = 200 s. The curve
also keeps a fast rise trend from 𝑡 = 200 s to 𝑡 = 350 s and
maintains the fall from 𝑡 = 350 s to 𝑡 = 500 s. The curve of
ESVS keeps lower level than that of SURFNet and has less
peak value (2.87 s) than that (3.32 s) of SURFNet.
Figure 5 shows the variation process of two curves with
increasing number of nodes. The curve of SURFNet has a rise
trend with relatively severe fluctuation and reaches peak value
(2.95 s) when the number of nodes is 160. The curve of ESVS
also has severe fluctuation in the rise process but keeps lower
level than that of SURFNet and has lower peak value (2.53 s)
than that of SURFNet.
In SURFNet, forwarding the request message relies on
the nodes in the AVL tree; namely, the nodes in tree make
use of the predefined parent-child relationship to relay the
request messages. The more the number of relay nodes is,
the longer the startup delay is. SURFNet can obtain low
lookup delay by making use of the AVL tree structure. With
the increase in the number of nodes, the amount of video
streaming in network also quickly increases, which leads to
the network congestion. Therefore, the startup delay results
of SURFNet show a fast rise trend from 𝑡 = 200 s to 𝑡 = 400 s.
Because 50 nodes quit the system after they play 4 video
files, the decrease in the network congestion level enables the
startup delay to quickly decrease. ESVS analyzes the content
similarity between videos and the playback behaviors of users
to calculate the contact level between videos. The videos
which have close contact are clustered into a chain-based tree
structure. The nodes which have joined the system form the
communities corresponding to the played videos. Because the
videos in the chains have high access probabilities with each
other, the request messages only experience two relay nodes,
ensuring low lookup delay. Moreover, the communities may
have multiple associate broker members, which reduces the
“distance” between the requesters and suppliers and obtains
low startup delay. The request nodes in ESVS estimate the
communication quality of transmission with the suppliers,
which ensures the low transmission delay. Because the
resource receivers can disconnect from current suppliers and
(2) Startup Delay. The difference values between the time of
sending request message and receiving first video data are
defined as the startup delay. The mean values of startup delay
during a time interval 50 s and the process of node joining the
system are shown in Figures 4 and 5, respectively.
As Figure 4 shows, the two curves corresponding to
ESVS and SURFNet have two fluctuation processes during
the whole simulation time. The blue curve of SURFNet
experiences a slight fluctuation from 𝑡 = 50 s to 𝑡 = 200 s,
starts to quickly increase from 𝑡 = 200 s to 𝑡 = 400 s, and
shows the fast fall from 𝑡 = 400 s to 𝑡 = 500 s. The red curve
International Journal of Distributed Sensor Networks
9
0.6
Packet loss rate
0.5
0.4
0.3
0.2
0.1
0
50
100
150
200 250 300 350
Simulation time (s)
400
450
500
SURFNet
ESVS
Figure 6: PLR against simulation time.
0.5
0.45
Packet loss rate
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
20
40
60
80
100 120 140
Number of nodes
160
180
200
SURFNet
ESVS
Figure 7: PLR against number of nodes.
research new suppliers when the communication quality in
the transmission path decreases, the interval time relieves the
congestion degree (the congestion time and level of ESVS are
lower than those of SURFNet). Therefore, the performance of
startup delay of ESVS is better than that of SURFNet.
(3) PLR. The ratio between the number of packets lost in the
process of video data transmission and the total number of
packets sent denotes PLR. The mean values of PLR during a
time interval 50 s and the process of node joining the system
are shown in Figures 6 and 7, respectively.
As Figure 6 shows, the blue curve of SURFNet has a fast
rise from 𝑡 = 50 s to 𝑡 = 350 s, keeps a decrease trend from
𝑡 = 350 s to 𝑡 = 500 s, and reaches the peak value (0.49) at
𝑡 = 350 s. The red curve of ESVS experiences a fast rise with
severe fluctuation from 𝑡 = 50 s to 𝑡 = 400 s, reaches the peak
value (0.38) at 𝑡 = 400 s, and decreases 𝑡 = 400 s to 𝑡 = 500 s.
The curve of ESVS keeps lower levels than those of SURFNet.
Figure 7 illustrates the variation of two curves corresponding to ESVS and SURFNet with increasing the number
of nodes. The curve of SURFNet keeps the fast trend with two
fluctuations and reaches the peak value (0.43) when the number of nodes is 160. Although the red curve corresponding to
the ESVS results also experiences the fluctuation, it shows a
stable increase process relative to that of SURFNet and the
increment and peak value (0.369) are lower than those of
SURFNet.
The nodes in SURFNet do not consider the communication quality in the data transmission path. The performance
of data transmission easily is subjected to the severe negative
influence due to the change of wireless mobile network
topologies. For instance, the network congestion leads to fast
increase of PLR results of SURFNet (the curve of SURFNet
keeps high level from 𝑡 = 200 s to 𝑡 = 350 s). The nodes
in ESVS estimate the communication quality of data transmission path before the construction of connection with
suppliers in order to obtain the optimal performance of
content delivery, which ensures the low PLR. Moreover,
when the resource receivers find the decrease of content
delivery (if the PLR in current transmission path is larger than
𝑇𝑙 = 0.35), they disconnect from the suppliers and research
new suppliers, which reduces the packet loss. Therefore, the
PLR results of ESVS are better than those of SURFNet. In
particular, because the network congestion may result in the
large number of packet losses, the packet loss rate may have
been larger than 0.35 (e.g., 0.4 or 0.5) when the receivers find
the decrease of transmission performance so that the peak
value (0.369) of ESVS curve is greater than 𝑇𝑙 = 0.35.
(4) Maintenance Cost. The messages used by maintaining the
P2P network such as nodes joining, leaving, and searching
are considered control messages. The occupied bandwidth
per second of control messages is defined as the maintenance
cost.
Figure 8 illustrates the variation of maintenance cost
results of ESVS and SURFNet with the increase in the
simulation time. The blue curve corresponding to SURFNet
results keeps the fast rise with severe fluctuation from 𝑡 = 50 s
to 𝑡 = 400 s, reaches the peak value (4.8) at 𝑡 = 400 s, and
gradually decreases from 𝑡 = 4000 s to 𝑡 = 500 s. The red
curve corresponding to ESVS results has a slow increase with
slight fluctuation from 𝑡 = 50 s to 𝑡 = 250 s, maintains fast
rise from 𝑡 = 250 s to 𝑡 = 400 s, and shows a decrease trend
from 𝑡 = 4000 s to 𝑡 = 500 s. The ESVS curve has lower level
than that of SURFNet and the peak value (4.3) of ESVS also
is less than that of SURFNet.
The maintenance cost of SURFNet mainly includes the
exchange of state messages of nodes in the AVL tree, the
state management of members in the chain attached to tree,
forwarding and handling the request messages of resources,
and the assignment of suppliers. The nodes in the tree have
longer online time than those of nodes in the chain; the
maintenance cost for the tree is low relative to the chain. In
initial simulation, the maintenance cost of SURFNet keeps
the slight increase. With the increase in the number of nodes
and corresponding resource demand, the maintenance cost
of SURFNet quickly increases. Because some nodes quit the
system, the decrease in the number of request messages
and node state lead to the fast fall of the maintenance cost
of SURFNet. The dynamic change of node state is mainly
10
International Journal of Distributed Sensor Networks
5
Maintenance cost (kb/s)
4.5
4
3.5
3
2.5
2
1.5
1
50
100
150
200 250 300 350
Simulation time (s)
400
450
500
SURFNet
ESVS
Figure 8: Maintenance cost against simulation time.
the reason for the maintenance cost level of the system.
The frequent change of state of nodes in the tree leads to
the increase in the frequency of tree reconstruction, which
results in the increase in the tree maintenance cost. The
resource lookup failure indicates that the request nodes find
that the resource demand cannot be met after the traversal
of request messages in the whole tree, which enables the
request messages to consume the large number of network
bandwidth. Although ESVS also employs the chain-based
tree structure, the tree scale (height and weight of tree) keeps
low level. This is because the chain includes the videos which
have close contact with the videos in the tree. Flattening
tree structure reduces the maintenance cost of tree and the
negative influence caused by the state churn of nodes in
tree, which also reduces the number of forwarding request
messages. Some node communities have multiple broker
members or associate broker members, which increases the
message cost of state maintenance. However, the small scale
of (associate) broker members relative to the community
members only results in the slight rise of maintenance cost.
These (associate) broker members not only share responsibility for the maintenance cost of the whole P2P network and
improve system scalability, but also speed up the resource
lookup process and reduce the number of forwarding request
messages. Moreover, sharing prefetched resources between
community members promotes the resource lookup success
rate, which further reduces the number of forwarding request
messages. Therefore, the performance of ESVS maintenance
cost is better than that of SURFNet.
5. Conclusion
In this paper, we propose a novel energy-efficiency socialinspired video sharing solution in wireless networks (ESVS).
ESVS builds a model to estimate the access probabilities
between videos in terms of the similarity of video content and
user playback behaviors. ESVS makes use of the access probabilities to cluster videos into a chain-based tree structure
and group the nodes into the communities corresponding
to the videos in the chain-based tree. In order to improve
the sharing efficiency of video content, ESVS designs a hybrid
resource lookup algorithm including push and pull and
makes use of the sharing of prefetched content to improve
the shortness of available resources in P2P networks. ESVS
further enables the receivers of video data to dynamically
regulate the suppliers in terms of the communication quality
in the data transmission path, which not only improves QoE
and energy endurance of mobile nodes, but also reduces
the consumption of network bandwidth. Simulation results
also show that ESVS obtains higher resource lookup success
rate, lower startup delay, less packet loss rate, and lower
maintenance cost than SURFNet.
Conflict of Interests
The authors declare that there is no conflict of interests
regarding the publication of this paper.
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant nos. 61501216,
61502219, 61402303, and 61522103, the Beijing Natural Science
Foundation (4142037), and the Science and Technology Key
Project of Henan province (152102210331 and 152102210332).
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