CN105007541B - Telescopic video flowable state multi code Rate of Chinese character multicast optimization transmission method - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
- H04N21/2343—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
- H04N21/234327—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements by decomposing into layers, e.g. base layer and one or more enhancement layers
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/238—Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
- H04N21/2383—Channel coding or modulation of digital bit-stream, e.g. QPSK modulation
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/266—Channel or content management, e.g. generation and management of keys and entitlement messages in a conditional access system, merging a VOD unicast channel into a multicast channel
- H04N21/2662—Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities
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Abstract
The present invention provides a kind of telescopic video flowable state multi code Rate of Chinese character multicast optimization transmission methods based on chance routing and network code, the chance routing mechanism and network coding technique of the method combination relay node forwarding, the time-varying dynamic characteristic of wireless network has been taken into account simultaneously, the competitive relation of wireless relay nodes and the code stream issue of priority of video coding layer, to meet the needs of scalable video interlayer dependence in real time, using complete distributed code rate allocation method, the final dynamic code rate resource allocation for realizing extensible video stream multi code Rate of Chinese character cast communication, dynamic transmission is dispatched and the combined optimization of dynamics route selection.The present invention improves the handling capacity of network entirety and the adaptability to time varying channel states, and more preferably video quality is provided for receiving terminal.
Description
Technical Field
The invention relates to a method in the technical field of data communication, in particular to a scalable video stream dynamic multi-rate multicast optimal transmission method based on opportunistic routing and network coding.
Background
With the rapid increase of mobile data traffic and the increasing popularity of intelligent terminal devices, wireless video streaming media technology has been increasingly widely applied in recent years. The analytic reports of global mobile data by cisco, usa, indicate that by the end of 2014, mobile video data traffic has accounted for more than 50% of the total mobile data traffic, and this ratio is expected to reach 75% by the end of 2019. However, data transmission in wireless networks is often susceptible to time-varying characteristics of wireless channels and transmission errors, and thus how to provide quality of service guarantees for video transmission in wireless networks remains a challenging problem.
As an important means for video content distribution in a network, a multi-rate multicast technology for Scalable Video Coding (SVC) streaming media can adapt to different user viewing requirements and heterogeneous network conditions to improve the transmission efficiency of the network. The scalable video coding bitstream includes a base layer and a plurality of enhancement layers, and these flexible multi-dimensional hierarchies provide a plurality of access points in three dimensions of spatial resolution, temporal frame rate, and video reconstruction quality. When the scalable video coding stream is transmitted in a multi-rate multicast mode, different IP multicast groups transmit the scalable video coding layers, and each receiver selectively adds a certain number of multicast groups according to different processing capacities and different link capacities, so that a video image sequence of the same content under different scale combinations is obtained.
Through the search discovery of the prior art, j.zhao et al published an article entitled "lay: Layered overlay multicast with Network coding" (LION) in IEEE Transactions on multimedia, oct.2006, pp.1021-1032, (institute of electrical and electronics engineers, multimedia bulletin, 2006, 10, 1021-. It is noted that this article is mainly based on the assumption that static network characteristic parameters (e.g. topology, link capacity, etc.) and do not change over time, and that one or more transmission paths from the source node to each end user must be predetermined before NUM optimization modeling and solving can be performed. However, in an actual wireless video transmission network, due to the time-varying characteristic of a wireless channel, the characteristic parameters of the wireless network often change frequently with time, so the optimal rate allocation scheme proposed by the article and suitable for the static network assumption cannot obtain the optimal video transmission performance.
It is found through retrieval that k.zeng et al published an article entitled "Capacity of opportunistic routing in multi-rate and multi-hop wireless networks" (multi-rate and Capacity of opportunistic routing in multi-hop wireless networks) in IEEE Transactions on wireless communication, dec.2008, pp.5118-5128 (institute of electrical and electronics engineers, wireless communication, 2008, 12 th, 5118 th page 5128), which indicates that the opportunistic routing method can dynamically select a next-hop node for a relay node of packet transmission by using the broadcast characteristic and spatial diversity characteristic of a wireless shared medium, and greatly improves the end-to-end transmission throughput of a wireless time-varying network compared with the conventional static routing mechanism. However, the method proposed in this article only studies a unicast scenario from a source node to a specific terminal node in a network, and does not consider how to implement multicast transmission optimization from the source node to multiple heterogeneous terminal users by using an opportunistic routing mechanism.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a scalable video stream dynamic multi-rate multicast optimal transmission method based on opportunistic routing and network coding, which combines an opportunistic routing mechanism and a network coding technology of relay node forwarding, and simultaneously considers the time-varying dynamic characteristics of a wireless network, the competition relationship of wireless relay nodes and the problem of the code stream priority of a video coding layer so as to meet the requirement of dependency among scalable video coding layers in real time.
In order to achieve the above object, the present invention provides a scalable video stream dynamic multi-rate multicast optimized transmission method based on opportunistic routing and network coding, comprising the following steps:
the method comprises the steps that firstly, at a source node, time is segmented according to the time-varying characteristics of a wireless network, a video stream is coded into a plurality of scalable video coding layers in each time period by using a scalable video coding technology, and interlayer dependency constraint conditions of scalable video coding are guaranteed by allocating code rates of different scalable video coding layers;
secondly, when a specific video data packet is forwarded by a relay node in the network at the relay node, dynamically selecting a next hop forwarding node for the specific video data packet by using an opportunistic routing mechanism, and further improving the throughput of the network by using a network coding technology;
thirdly, in the wireless network, dividing each wireless network node in the wireless shared medium into a plurality of maximum simultaneous transmission node sets, and realizing the optimal scheduling of the overall transmission time of the network among the maximum simultaneous transmission node sets;
fourthly, combining the interlayer dependency conditions of the video coding layer at the source node, the opportunistic routing mechanism and the network coding technology forwarded by the relay node, the time-varying dynamic characteristics of the wireless network and the node transmission competition relationship obtained in the previous three steps, and using a multi-time-period dynamic network utility maximization modeling method to realize the optimization problem of scalable video streaming dynamic multi-code rate multicast communication based on opportunistic routing and network coding;
when a specific code rate allocation scheme is carried out for the terminal node, a fully distributed code rate allocation method is adopted, and finally, the joint optimization of dynamic code rate resource allocation, dynamic routing selection and dynamic transmission scheduling of the scalable video stream multi-code rate multicast communication is realized.
Preferably, in the first step, the time segments are further divided into scalable video stream multicast time, and the channel state information of the wireless channel is relatively stable in each time segment and is obtained by a direct measurement and channel state prediction method.
Preferably, in the first step, the inter-layer dependency constraint of scalable video coding is that the decoding of a higher-labeled video layer needs to depend on a lower-labeled video layer, that is, the multicast transmission order of the video layers needs to be performed in the order of the labels of the video layers from lower to higher.
Preferably, in the second step, the opportunistic routing mechanism dynamically selects an actual next hop node for the data packet in the next hop candidate node set of the relay node by using the broadcast characteristic and the spatial diversity characteristic of the wireless shared medium.
Preferably, in the second step, the network coding technique is to perform arithmetic coding operation on the data packet before the relay node forwards, and specify the actual bandwidth consumption on each link as the maximum value of the bandwidth consumed on the link by all destination nodes; the condition is a constraint condition of adopting network coding on a link so as to realize resource sharing of different destination nodes on the same link.
Preferably, in the third step, when all nodes in the set of simultaneous transmission nodes perform data packet transmission simultaneously, their associated links will not generate wireless contention and can successfully complete the data packet transmission to the next hop node.
Preferably, in the third step, the maximum set of simultaneous transmission nodes is a set of simultaneous transmission nodes, and if any node is added to the set, the set is changed into a set of non-simultaneous transmission nodes.
Preferably, in the fourth step, the joint optimization problem is: the method comprises the steps of taking the overall quality of videos received by all users in a complete time period of scalable video stream multi-rate multicast as a target function, considering the interlayer dependency relationship of scalable video stream decoding, and establishing the dynamic rate resource allocation, routing selection and transmission scheduling joint optimization problem of scalable video stream dynamic multi-rate multicast communication by taking an information stream balance condition, a network coding condition, a maximum simultaneous transmission node set time scheduling condition and the limitation of an opportunistic routing mechanism on channel capacity as constraint conditions.
Preferably, in the fourth step, the method for allocating fully distributed code rates refers to: the original optimization problem is decomposed into a plurality of sub-optimization problems by applying a dual decomposition theory, each network node is allowed to dynamically adjust and update the code rate, transmission scheduling and routing selection by using local information, and iterative solution is performed in a distributed mode, so that the dynamic optimal resource allocation of the network is realized.
More preferably, the specific implementation steps of the fully distributed code rate allocation method are as follows:
(a) initialization: setting the initial primitive/dual variables to some non-negative values;
(b) repeatedly executing:
for the source node:
b1) receiving dual variable information from a destination node;
b2) receiving dual variable information from a next hop node;
b3) acquiring stored original and dual variable information from a local memory;
b4) updating original/dual variables;
b5) transmitting the updated dual variable information to the network node set;
for each node:
b1) receiving dual variable information from a source node;
b2) receiving dual variable information from a next hop node;
b3) acquiring stored original and dual variable information from a local memory;
b4) updating original/dual variables;
b5) transmitting the updated dual variable information to a transmitting node of the node;
b6) if the node is the destination node, transmitting the updated dual variable information to the source node;
and stopping the fully distributed code rate allocation method until all the original and dual variables converge to the optimal value or the maximum iteration times are reached.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a completely distributed code rate allocation method for the requirements of a dynamic time-varying wireless network, realizes dynamic network routing selection and transmission scheduling, improves the overall throughput of the network and the adaptability to time-varying channel states by introducing opportunistic routing and network coding, and provides better video quality for a receiving end.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a diagram of a multicast network according to an embodiment of the present invention, and illustrates basic modules of opportunistic routing;
FIG. 3 is a diagram illustrating a collision relationship between transmission nodes in a network according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a distributed code rate allocation method according to an embodiment of the present invention;
fig. 5 is a schematic diagram of time scheduling of a maximum simultaneous transmission node set in a network according to an embodiment of the present invention, where (a) a curve is given for a relationship between a link average packet receiving rate in the network and a change of each time period; (b) as shown, the optimal time scheduling result for the maximum set of simultaneously transmitted nodes in the network is given.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
The embodiment provides a dynamic multi-rate multicast optimized transmission method for scalable video streams based on opportunistic routing and network coding, which refers to a method flowchart shown in fig. 1 and includes the following steps:
1. scalable video stream rate allocation constraint at source node
As shown in fig. 2, the present embodiment performs example analysis on the multicast network structure, and may abstract the wireless network into a directed graph G ═ (V, E), where V is a set of wireless nodes and E is a set of wireless links1，L2，...，LMWherein the transmission code rate of the m layer is in the tolerance intervalAnd then multicast transmitting the layered scalable video stream to each destination node through the multi-hop wireless relay node by using an opportunistic routing mechanism and network coding operation. Furthermore, the entire time period of scalable video streaming is evenly divided into several equal time periods T e {1, 2.. T } corresponding to the time-varying characteristics of the wireless link, and the channel state of the wireless link remains unchanged in each time period.
2. Opportunistic routing mechanisms and network coding operations at relay nodes
As shown in fig. 2, the basic modules of the opportunistic routing mechanism are also shown. Using opportunistic routing mechanisms, a wireless packet transmission sent by a sending node can be received by multiple neighboring nodes within its effective transmission range. Since no specific next hop forwarding node is specified, it is possible for any next hop node that successfully receives the packet to continue forwarding the packet. Thus, the opportunistic routing mechanism selects as a set of forwarding nodes for a sending node a next hop node that is capable of receiving a data packet and that is geographically closer to a destination node than the sending node, and dynamically selects an actual next hop forwarding node for a different data packet, thereby making efficient use of the broadcast characteristics of the wireless shared medium through local opportunistic forwarding.
Suppose that during time period t, node s needs to send packet P1、P2And P3Multicast transmission to destination node d1～d3. All next hop neighbor nodes n of node s1～n6Are within their effective transmission range and are therefore able to receive all three data packets sent by node s. However, among the six next-hop neighbor nodes, since only n is present1、n2And n3Geographically closer to the destination node than node s, so that the set of three nodes n1，n2，n3The set of forwarding nodes selected as nodes s, denoted as Fs(t) of (d). In fig. 2, it is further assumed that nodes s and F are connecteds(t) the link of each forwarding node has a packet reception probability of 2/3. Since wireless data packet reception between different receiving nodes is highly uncorrelated, it is assumed that after three data packet transmissions, the received data packet situation at each node is as shown in fig. 2, i.e. n1Receiving a data packet P1And P2，n2Receiving a data packet P1And P3，n3Receiving a data packet P2And P3. Next, a set of n forwarding nodes is formed1，n2，n3The received data packet is further forwarded to three destination nodes through an opportunistic routing mechanism. In particular, with destination node d2Consider, as an example, Fs(t) middle node and d2Of the connectivity relation, node n2And n3The received data packet needs to be forwarded to d2. If node n2And n3There is no communication between them, both nodes expect to forward the packet P3Thereby possibly leading to destination node d2For data packet P3Is received repeatedly. To avoid this, the conventional opportunistic routing mechanism would be based on node n2And n3To destination node d2The corresponding forwarding priority is allocated to the spatial distance, and if the same data packet is successfully received by a plurality of forwarding nodes, an actual forwarding node is selected to continue forwarding the data packet according to the forwarding priorities of the forwarding nodes. In this example, since n2Ratio n3Closer to destination node d2，n2Has a forwarding priority higher than n3. Thus, if node n2If a packet is successfully received, it will be responsible for forwarding the packet to the destination node d2And node n3Then duplicate transmissions are avoided; on the contrary, if the node n2Node n without receiving the data packet3If the data packet is successfully received, node n3Will be responsible for forwarding the packet to destination node d2. Thus, node n2Will forward the data packet P1And P3To destination node d2And node n3Forwarding only data packets P2To destination node d2. Similarly, this forwarding node set selection and prioritization strategy may be applied to transmissions of the other two destination nodes. However, it should be noted that a limitation of conventional opportunistic routing mechanisms is the need to provide a specific MAC protocol to determine the selection of a set of forwarding nodes and to coordinate the coordinated transmission between the nodes in the set. In addition, applying the conventional opportunistic routing mechanism from a unicast scenario to a multicast scenario of multiple destination nodes makes the design of the corresponding MAC protocol more complicated.
In contrast, by introducing network coding operations, forwarding relay nodes may perform on received data packetsRow algebra arithmetic operates to overcome the two limitations of the opportunistic routing mechanisms mentioned above. For example, instead of forwarding a received data packet directly, node n1Can forward the coded data packet P1+P2To destination node d1Node n3Can forward the coded data packet P2+P3To destination node d2And d3And node n2Still forwarding data packet P1And P3. Therefore, by using network coding operation in the multicast scenario, all three destination nodes can successfully recover three original data packets. The benefit of combining the opportunistic routing mechanism with the network coding operation is that the destination node no longer needs to receive all the original data packets, instead, only needs to receive any K linearly independent coded data packets (assuming that the number of original data packets is K) to successfully recover the original K data packets.
3. Maximum simultaneous transmission node set partitioning and scheduling for wireless networks
Fig. 3 is a diagram illustrating a collision relationship between transmission nodes in the original network topology shown in fig. 2. In the original network topology and scalable video multicast network scenario shown in FIG. 2, nodes s, n1、n2And n3May be selected as a transmitting node to send or forward the data packet. However, due to the collision of the transmission nodes, the four nodes cannot transmit or forward the data packet at the same time. In the transmission node collision relationship diagram shown in fig. 3, each endpoint represents a transmission node in the original network topology and has a set of link sets connected to its set of forwarding nodes. If two transmission nodes cannot simultaneously transmit data packets due to collision between their associated links, a collision relationship exists between the two transmission nodes, corresponding to a connection line between two endpoints in a collision relationship graph. On the basis of which the concept of a set of simultaneous transmission nodes is introduced. When all nodes in the simultaneous transmission node set carry out data packet transmission simultaneously, the associated links can successfully finish the next hop without generating wireless competitionAnd transmitting data packets of the points. The basic idea of a set of simultaneous transmission nodes is to avoid collisions between transmission nodes by requiring that all opportunistic routing receivers in the set do not have a contention relationship with each other at the same time. In order to fully utilize the wireless shared medium to achieve the throughput ceiling of the network, a set of maximum simultaneous transmission nodes is defined as a set of simultaneous transmission nodes, which if added to any one node in the set results in the set becoming a set of non-simultaneous transmission nodes.
4. Establishing a multi-time-period dynamic network utility maximization problem based on opportunistic routing and network coding, and providing a distributed code rate allocation method
The problem of establishing a multi-time period dynamic network utility maximization is as follows (wherein the meaning of each parameter can be correspondingly obtained in the context):
constraint conditions are as follows:
wherein the optimization variables are:
r-represents a vector consisting of video reception rate. Specifically, Rmd(t) represents the bitrate at which the destination node d receives the mth video layer in the tth time period.
g-represents a vector consisting of the virtual information stream code rates. In particular, the amount of the solvent to be used,indicating the virtual information stream bitrate for the destination node d to receive the mth video layer on the wireless link (i, j) during the tth time period.
f-represents a vector consisting of the actual bandwidth consumption. In particular, the amount of the solvent to be used,represents the actual amount of bandwidth consumed on the wireless link (i, j) for receiving the mth video layer during the tth time period.
λ -denotes a vector consisting of time scales. In particular, λα(t) indicates a particular set of maximum simultaneous transmitting nodes Г during the tth time periodα(t) the allocated transmission time ratio.
The optimization target is as follows:
the sum of utility functions of all users in the wireless multicast network is maximized, that is, the overall received video quality of the users in all the T scalable video stream multicast periods is maximized. The user utility function U (R)md(t)) represents the destination node d at the code rate Rmd(t) a reduction amount of video distortion after successfully receiving and decoding the mth video layer.
The constraint conditions are as follows:
2) and network coding constraint conditions are used for ensuring that the actually consumed bandwidth on the wireless link is the maximum value in the virtual information flow from the link to all destination nodes.
3) The transmission scheduling condition for the set of maximum simultaneous transmission nodes specifies that at most one set of maximum simultaneous transmission nodes can be scheduled for transmission at any time.
4) Wireless broadcast transmission capacity constraints based on opportunistic routing mechanisms and network coding operations, where J is the set of forwarding nodes F for node ii(t) a subset of nodes, the super edges (i, J) representing the set of links from node i to the broadcast area formed by the nodes in J, C(i，J)Is the link effective transmission code rate of the broadcast area composed of nodes from node i to J.
5) And 6) is an inter-layer dependency constraint for scalable video coding that ensures that all destination nodes receive video coding layers in an order of increasing index from low to high.
The distributed solving algorithm and the execution process of the optimization problem are as follows:
firstly, obtaining the Lagrangian dual form of the optimization problem as follows:
wherein,is a lagrange multiplier.
Further, using an original-dual algorithm, updating the original variable and the dual variable simultaneously, and gradually approaching the optimal point through iteration, wherein k represents the iteration number, and delta is a positive step value [ ·]+Represents an operation taking a positive value:
wherein, the constraint condition corresponding to the dual variable μ is an equality constraint condition, so that the corresponding update iteration does not need to carry out [ ·]+And (6) operation.
As shown in fig. 4, the fully distributed bitrate allocation method is performed as follows (where the meaning of each parameter can be obtained in context):
(a) initialization: setting initial primitive/dual variablesCertain non-negative values;
(b) repeatedly executing:
for a source node s:
b1 receiving a dual variable from the destination node d
b2 receiving dual variables from the next hop node j
b3 obtaining the stored video receiving code rate R from the local memorymd(t)|kDual variablesvmd|k、γmd|k、β(s，J)(t)|k、θ(t)|kAnd link effective transmission code rate
b4 updating to obtain video receiving code rate Rmd(t)|k+1Virtual information stream code rateActual bandwidth consumptionTransmission time ratio lambdaα(t)|k+1And dual variables
b5 transmitting dual variable θ (t) to network node set { N ∪ D }k+1。
For each node i ∈ N ∪ D:
① receiving dual variable θ (t) from source node sk；
② receives a dual variable from the next hop node j
③ retrieving stored dual variables from local memoryβ(i，J)(t)|kAnd link efficient transport codes
④ updating to obtain virtual information flow code rateActual bandwidth consumptionTransmission time ratio lambdaα(t)|k+1And dual variables
⑤ transmitting a dual variable to a sending node in { j ∈ V | (j, i) ∈ E }
⑥ if node i is the destination node, i.e., i ═ D, D ∈ D, then the dual variable is transmitted to the source node s
Until: all original and dual variables converge to an optimal value or until a maximum number of iterations is reached.
As shown in fig. 5 (a), a curve of the average link packet receiving rate in the network with time period variation is given, and as shown in fig. 5 (b), an optimal time scheduling result of the maximum simultaneous transmission node sets in the network is given, wherein, corresponding to fig. 2 and fig. 3, the three maximum simultaneous transmission node sets are Г respectively1(t)＝{s}，Г2(t)＝{n2} and Г3(t)＝{n1，n3}. it can be seen that since the source node s is always generated from the packet by scalable video coding, it is Г in each time period no matter how good the channel status is1(t) the allocated optimal transmission ratio always keeps a large value (about 0.4). on the other hand, when the network conditions are good and the packet reception rate is relatively large, the proposed code rate allocation method can be applied to transmission nodes with more next-hop nodes (e.g. Г)2Node n in (t)2) On the contrary, when the network condition is not good, the receiving rate of the data packet is relatively small, and the proposed code rate allocation method can improve the utilization rate of other transmission nodes (for example Г)3Node n in (t)1And n3) To combine multiple unreliable wireless links into a more reliable link.
The invention establishes the problem of maximization of the utility of the multi-time-period dynamic network based on the opportunistic routing and the network coding for the needs of the dynamic time-varying wireless network, correspondingly provides a fully distributed code rate allocation method, realizes dynamic scalable video stream code rate allocation, network routing selection and transmission scheduling, improves the overall throughput of the network and the adaptability to the time-varying channel state through the introduction of the opportunistic routing and the network coding, and provides better video quality for a receiving end.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (9)
1. A dynamic multi-rate multicast optimized transmission method for scalable video streams is characterized by comprising the following steps:
the method comprises the steps that firstly, at a source node, time is segmented according to the time-varying characteristic of a wireless network, a video stream is coded into a plurality of scalable video coding layers by using a scalable video coding technology in each time period, and interlayer dependency constraint conditions of scalable video coding are guaranteed by allocating code rates of different scalable video coding layers;
secondly, at a relay node, when a specific video data packet is forwarded by the relay node in the network, an opportunistic routing mechanism is used for dynamically selecting a next hop forwarding node for the specific video data packet, and a network coding technology is used for further improving the throughput of the network;
thirdly, in the wireless network, dividing each wireless network node in the wireless shared medium into a plurality of maximum simultaneous transmission node sets according to the time-varying dynamic characteristics and the node transmission competition relationship of the wireless network, and realizing the optimal scheduling of the overall transmission time of the network among the maximum simultaneous transmission node sets; the maximum simultaneous transmission node set is a simultaneous transmission node set, and if any node is added into the set, the set is changed into a non-simultaneous transmission node set;
fourthly, combining the interlayer dependency constraint conditions of the video coding layer at the source node, the opportunity routing mechanism and the network coding technology forwarded by the relay node, the time-varying dynamic characteristic of the wireless network and the node transmission competition relationship obtained in the previous three steps, and using a multi-time-period dynamic network utility maximization modeling method to realize the optimization problem of the scalable video streaming dynamic multi-code rate multicast communication based on the opportunity routing and the network coding;
when a specific code rate allocation scheme is carried out for the terminal node, a fully distributed code rate allocation method is adopted, and finally, the joint optimization of dynamic code rate resource allocation, dynamic routing selection and dynamic transmission scheduling of the scalable video stream multi-code rate multicast communication is realized.
2. The scalable video stream dynamic multi-rate multicast optimized transmission method according to claim 1, wherein in the first step, the time segments are further divided into scalable video stream multicast time, the channel state information of the wireless channel is relatively stable in each time segment, and the channel state information is obtained by direct measurement and channel state prediction.
3. The scalable video stream dynamic multi-rate multicast optimized transmission method according to claim 1 or 2, wherein in the first step, the inter-layer dependency constraint of scalable video coding is: the decoding of the higher labeled video layer needs to be dependent on the lower labeled video layer, i.e. the multicast transmission order of the video layers needs to be in the order of the video layer labels from lower to higher.
4. The method of claim 1, wherein in the second step, the opportunistic routing mechanism is: and dynamically selecting an actual next hop forwarding node for the data packet in the next hop forwarding candidate node set of the relay node by utilizing the broadcasting characteristic and the space diversity characteristic of the wireless shared medium.
5. The scalable video stream dynamic multi-rate multicast optimized transmission method according to claim 1 or 4, wherein in the second step, the network coding technique is: performing arithmetic coding operation on the data packet before the relay node forwards the data packet, and specifying the actual bandwidth consumption on each link as the maximum value of the bandwidth consumed by all destination nodes on the link; the condition is a constraint condition of adopting network coding on a link so as to realize resource sharing of different destination nodes on the same link.
6. The method of claim 1, wherein in the third step, when all nodes in the simultaneous transmission node set perform data packet transmission simultaneously, their associated links will not generate wireless contention and can successfully complete data packet transmission to the next hop node.
7. The dynamic multi-rate multicast optimized transmission method for scalable video streams of claim 1, wherein in the fourth step, the joint optimization is: the method comprises the steps of taking the overall quality of videos received by all users in a complete time period of scalable video stream multi-rate multicast as a target function, considering the interlayer dependency relationship of scalable video stream decoding, and establishing the dynamic rate resource allocation, routing selection and transmission scheduling joint optimization problem of scalable video stream dynamic multi-rate multicast communication by taking an information stream balance condition, a network coding condition, a maximum simultaneous transmission node set time scheduling condition and the limitation of an opportunistic routing mechanism on channel capacity as constraint conditions.
8. The scalable video stream dynamic multi-rate multicast optimized transmission method according to claim 1 or 7, wherein in the fourth step, the fully distributed rate allocation method is: the original optimization problem is decomposed into a plurality of sub-optimization problems by applying a dual decomposition theory, each network node is allowed to dynamically adjust and update the code rate, transmission scheduling and routing selection by using local information, and iterative solution is performed in a distributed mode, so that the dynamic optimal resource allocation of the network is realized.
9. The dynamic multi-rate multicast optimized transmission method for scalable video streams according to claim 8, wherein the fully distributed rate allocation method specifically comprises the following steps:
(a) initialization: setting the initial primitive/dual variables to some non-negative values;
(b) repeatedly executing:
for the source node:
b1) receiving dual variable information from a destination node;
b2) receiving dual variable information from a next hop node;
b3) acquiring stored original and dual variable information from a local memory;
b4) updating original/dual variables;
b5) transmitting the updated dual variable information to the network node set;
for each node:
b1) receiving dual variable information from a source node;
b2) receiving dual variable information from a next hop node;
b3) acquiring stored original and dual variable information from a local memory;
b4) updating original/dual variables;
b5) transmitting the updated dual variable information to a transmitting node of the node;
b6) if the node is the destination node, transmitting the updated dual variable information to the source node;
and stopping the fully distributed code rate allocation method until all the original and dual variables converge to the optimal value or the maximum iteration times are reached.
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